The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future | Kevin Kelly


Kevin Kelly; The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future; Penguin Books, reprint; 2017-06-06; 338 pages; Amazon:0143110373: Kindle: $14, paper: $13+SHT.

tl;dr → A teleological megatrends framework; transformation unto The Beginning (The Singularity) themed around 12 gerunds. He hits all the notes, quickly. Gee Whiz! Storytelling from the origins of the Internet in the ’90s. Some of the notes are passe now here in 2017-H2 (e.g. Uber is no longer cool, RSS still exist but it is no longer “a thing”, Software-as-a-Service is acronymed as SaaS, not SaS, Narrative performed Swedish-style “reorganization” in 2016-H2 [they hold & perform all customer videos]).

Listicle

  1. Becoming
  2. Cognifying
  3. Flowing
  4. Screening
  5. Accessing
  6. Sharing
  7. Filtering
  8. Remixing
  9. Interacting
  10. Tracking
  11. Questioning
  12. Beginning

Mentions

  • The gerunds
    • present participles
    • continuous action
  • Moving away from nouns, towards verbs, page 6
    flows contra stocks [John Hagel?]
  • digital
    • copies
    • bookkeeping (tracking)
    • (re-)analysis
  • utopia, dystopia → protopia
  • ‘B
  • Artificial Intelligence (requires)
    1. Cheap Parallel Computing
    2. Big Data (really very big biggie data)
    3. Better Algorithms
  • DeepMind, Google
  • Watson, IBM
  • Baxter, MIT
  • Robots are for
    1. Jobs humans can do but Robots can do even better
    2. Jobs humans can’t do but Robots can
    3. Jobs we didn’t know we wanted done
    4. Jobs only human can do – at first
  • Robots are for “The Three Ds”
    • Dirty
    • Dreary
    • Dangerous
  • Computers→ The Internet is for copies.
    <quote>The flow of copies is inevitable</quote>, page 62.
  • Manufacturing is about making cheap copies
  • Generations of computing
    1. The Desktop
    2. The Web (of pages and links)
    3. Streams
  • The Generatives [pages 68-70]
    1. Immediacy
    2. Personalization
    3. Interpretation
    4. Authenticity
    5. Accessibility
    6. Emobodiment
    7. Patronage
    8. Discoverability
  • Fixities
    1. Fixity of the page
    2. Fixity of the edition
    3. Fixity of the object
    4. Fixity of completion
  • Fluidities
    1. Fluidity of the page
    2. Fluidity of the edition
    3. Fluidity of the container
    4. Fluidity of growth
  • Flowing
    The stages of flowing

    1. Fixed. Rare.
    2. Free. Ubiquitous
    3. Flowing. Sharing.
    4. Opening. Becoming.
  • People of the
    • People of the Book
    • People of the Screen
  • Google Glass
  • Google Translate
  • Amazon Kindle Unlimited
  • Software as a Service (SaaS)
  • Adobe Photoshop
  • servicized
  • prosumer
  • Real-Time On Demand.
  • Amazon Home Services
  • UberPool
  • Co-working spaces (rent-a-desk, in a coffee bar).
  • Decentralization
    • decentralized “money” → Bitcoin [Ethereum]
    • mesh networks→ FireChat
  • Platform Synergy
    marketplaces, multi-sided marketplaces
  • Clouds
    rented computers, someone else’s rented computer.

    • Google Drive
  • Creative Commons
  • The degrees of “socialism” in stages of sharing [due to Shirkey]
    1. Sharing
    2. Cooperation
    3. Collaboration
    4. Collectivism
  • Sharing requires
    • Filters
    • Gatekeepers
    • Editors
    • Curators
  • <quote>inside every working anarchy there is an old-boy network.</quote>, attributed to Mitch Kapor, page 151.
  • Filtering done by
    • gatekeepers
    • intermediaries
    • curators
    • brands
    • government
    • cultural environment [cultural forces]
    • friends
    • ourselves
  • Filter Bubble
  • <quote>In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information onsumes. What information consumes is rather obvious:L it sonsumes the attention of its recipients. Hence a wealth of information creates a poverty of attention</quote>, attributed to Herbert Simon, 1971
    <quote>In a world of abundance, the only scarcity is human attention.</quote>
  • Attention
    • commodity attention
    • Attention cost runs $2-$3/hour for consumers across all media and across all recorded time <ahem>since 1990</ahem>
    • Naive econometrics done by Kelly 1995→2015.
  • Google AdSense
  • Something about decentralization in advertising:
    <quote>For instance, what if advertising followed the same trend of decentralization as other commercial sectors have? What if customers created, placed and paid for ads?</quote>, page 182.
    <ahem>These experiments have been run and we know the answer to them</quote>
  • Something about influencer marketing.
  • Remixing
  • SketchUp
  • “nondestructive editing”
    undo & redo
  • Virtual Reality
  • HoloLense, Microsoft
  • MagicLeap, (funded by) Google
  • “presence,” as in “tele presence”
  • Lego
  • Second Life
  • Project Sansa
  • Minecraft
  • Minority Report
    • Steven Spielberg
    • 2002
    • precrime, crime prediction
  • Google Nest
  • Apple Watch
  • Project Jaquard, (funded by) Google
  • The Squid, Northwestern University
    a shirt that measures posture.
  • The Sensory Substitution Vest
    • David Eagleman, neuroscience, Baylor  University
    • a shirt; vibration in lieu of sound.
  • Virtual Reality (VR)
    contra Augmented Reality (AR)

    • More Sesnses
    • More Intimacy
    • More Immersion
  • goggles
    the optical prostheses
  • game play, theory of game play
  • Quantified Self
  • Mathematica
  • personal analytics
  • personal baseline
  • Udo Wachter, 2004, a vibrating compas-in-belt
  • Lifestream
    • 1999
    • associative indexing, of media
    • Intellectual Property
      • David Gelertner
      • Eric Freeman
    • contra
      Apple Time Machine, a UX for the backup product.
  • Steve Mann
    • 1990s
    • MIT, now  University of Toronto
    • Cyborg camera
    • Quantimetric Self-Sensing, a branded term
  • Google Glass
  • Gordon Bell
    • Microsoft Research
    • 2000-2006, 1-minute photos
    • MyLifeBits
  • Narrative
    • Notice: Narrative is “transferring operations” to a new legal entity.
      • 2016-06 → “bankruptcy”
      • 2016-11 → “reorganization” under Swedish law.
    • You don’t own it, you just use it
      You don’t own your videos, you an play them until they cease operations.
    • Upload only, no download.
      Closed API, access via performative UX only.
  • Tracking done by
    • car movements
    • highway traffic
    • ride-share taxis
    • long-distance travel (air, train)
    • drones
    • postal mail
    • utilities
    • cell phone location,
      Call Data Record (CDR)
    • Civic cameras
    • Commercial spaces
      Private Spaces
    • Home automation, Smart Home
      records stored in someone else’s computers “in the cloud”
    • Home surveillance
    • Interactive devices
    • Loyalty cards
    • E-tailers
    • Internal Revenue Service  (IRS)
    • Credit cards
    • e-wallets
    • photo face recognition
    • web activities
    • social media
    • search, internet search
    • streaming
    • e-books
    • fitness trackers
  • Interactive Voice Response (IVR)
    • Siri, Apple
    • Now, Google
    • Cortana, Microsoft
    • Kinect, Microsoft
    • Television, Samsung
    • Television, Vizio
    • Echo, Amazon
  • Photo face recognition
    • Facebook
    • Google
  • Philip K. Dick, Minority Report
  • Ubiquitous tracking “is the dual of” Ubiquitous copying
  • Surveillance logisms & neologisms
    • Panopticon
    • Surveillance
    • Sousveillance
    • Co-veillance
  • Determinism & anthropomorphization
    Bits want to

    • move.
    • wabe linked to other bits.
    • recoked on  real time.
    • duplicated, replicated, copied.
    • be meta.
  • Bitcoin
  • Pretty Good Privacy (PGP)
  • The duality, the trade-off between
    • personalization
    • privacy
    • <quote>Vanity trumps privacy</quote>, page 262.
  • Co-veillance is a natural state [see quotes]
  • Anonymity
    • is bad in large doses; salubrious in tiny quantities,
    • shifts over time to pseudonymous,
    • counter with trust & transparency.
  • Quantity
    • has a quality all its own
    • “more is different”, attributed to J. Storrs  Hall
    • zillionics
      after “yotta-” is “zillion”
  • ,Maximum Likelihood Estimation (MLE)
  • Of Medi
    aUnbundling, unpacking, verticalization, specialization

    • classifieds → Craigslist
    • stock quotes → Yahoo! (Finance)
    • gossip → BuzzFeed
    • restaurant reviews → Yelp
    • stories blogs (linkbait) → everyone
  • Wikipedia
    • founded as Nupedia
    • rollback
      easier to rollback troll input than to create troll input
  • Emergent phenomenon
  • The Long Tail
  • The Shallows [Shirkey?]
  • Flux, depth and length of attention span; c.f. long-span serialized dramas.
  • Search [questioniing]
    • social expectation to have looked it up
    • associative indexing of everything [of everything relevant & not IP-limited]
  • Albert Einstein
    attributed for aphorisms on “good questions”
  • The Beginning (The Singularity)
    • noosphere, sphere of thought
    • global mind, hive mind
    • always on
    • “soft singularity” contra “hard singularity”
  • and

Quotable

  • <quote>In our era, processes trump products.</quote> page 6.
  • <quote>Particular technological processes will inherently favor particular outcomes.</quote>, page 7.
  • <quote><snip/>we can get the most from the technologies when we “listen” to the direction the techologies lean, and bend our expectations, regulatoins, and products to these fundamental tendencies within that technology. We’ll find it easier to manage the complexities, optimize the benefits, and reduce the harm of particular technologies when we align our uses with their biased trajectory</quote>, page 8.
  • <quote>The flow of copies is inevitable</quote, page 62.
  • <quote>In a real sense, these uncopyable values are things that are “better than free”. Free is good, but these are better since you’ll pay for themn. I call these qualities “generatives.” A generative value is a quality or attribute that must be generated at the time of the transaction. generative thing cannot be copied, cloned, stored and warehoused. A generative cannot be faked or replicated. It is generated uniquely, for that particular exchange, in real time. Generative qualities add value to free copies and therefore are something that can be sold. There are eight generative that are “better than free.” </quote>, page 68, page 68-70.
  • <quote>For eons and eons, humans have lived in tribes adn clans where every act was open and visible and there were no secrets. Our minds evolved with constant co-monitoring. Evolutionarily speaking, coveillance is our natural state. I believe that, contrary to our modern suspicions, there won’t be a backlash against a circular world in which we constantly track each other because humans have lived like this for a million years, and – if truly equitable and symmetrical – it can feel comfortable</quote>, page 262.

Who

the pantheon…
  • Ted Nelson
  • Geoff Hinton
  • Stephen Hawking
  • Rodney Brooks, MIT
  • Marshall McLuhan
  • Nicholas Carr
  • Brewster Kahle
  • Bill Gates
  • Ward Cunningham
  • John Perry Barlow
  • Clay Shirkey
  • Alvin Toffler
  • Larry Keeley, expert, innovation.
  • Howard Rheingold
  • Mitch Kapoor
  • Joseph Pine
  • Herbert Simon
  • Brian Arthur, Santa Fe Institute.
  • Paul Romer
  • Jaron Lanier
  • Rosalind Picard, Media Lab, MIT
  • Rana el Kaliouby, Media Lab, MIT
  • Steven Spielberg
  • John Underkoffler, Media Lab, MIT
  • David Eagleman, neuroscience, Baylor  University
  • Jaron Lanier
  • Gary Wolf
  • Larry Smarr
  • Stephen Wolfram
  • Nicholas Felton
  • UdoWachter
  • David Gelertner
  • Eric Freeman
  • Steve Mann
  • Camille Hartsell, research librarian to Kevin Kelly’ LinkedIn Twotter.
  • Philip K. Dick
  • David Brin
  • J. Storrs  Hall, nanotechnology, popularizatoin, boosterism, books; Wikipedia.
  • Albert Einstein
  • Pablo Picasso
  • William Fifield
  • H.J. Wells
  • Teilhard de Chardin

Biography

Kevin Kelly is Senior Maverick at Wired magazine. He co-founded Wired in 1993, and served as its Executive Editor for its first seven years. He is also founding editor and co-publisher of the popular Cool Tools website, which has been reviewing tools daily since 2003. From 1984-1990 Kelly was publisher and editor of the Whole Earth Review, a journal of unorthodox technical news. He co-founded the ongoing Hackers’ Conference, and was involved with the launch of the WELL, a pioneering online service started in 1985.
Via Amazon

Other Works

  • New Rules for the New Economy,
    on decentralized emergent systems,
  • Out of Control,
    a graphic novel about robots and angels
  • The Silver Cord,
    an oversize catalog of the best of Cool Tools (a web site, wiki, blog, thingy),
  • What Technology Wants.
    a summary of his theory of technological determinism.

Referenced

  • Clay Shirkey, Here Comes Everybody, 2008.
  • Joseph Pine, Mass Customization, 1992.
  • David Brin, The Transparent Society, 1999.

Stanford PDV 91 — How to Think Like a Futurist: Improve Your Powers of Imagination, Invention, and Capacity for Change

Signup

Syllabus

References

  • Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, ISBN:978-0143110378, paperback: 2017-06-06.
    Required.
  • Jane McGonigal, Reality is Broken, ISBN:978-0143120612,
    Recommended.
  • Rebecca Solnit, Hope in the Dark: Untold Histories, Wild Possibilities, ISBN 1608465764,
    Recommended.

First Assignment

<quote>A favorite saying of futurists is: “Get there early.” As futurists, we think about things long before they start to happen. Since our first class meeting is still in the future, this is the perfect opportunity for you to start getting there early.

Before our first class, please read the following two essays:

You’ll notice that our syllabus includes quotes throughout for inspiration and provocation. After you’ve read these two essays, please send me an email with the one sentence from each essay that stood out to you. (That is, please send Prof. McGonigal your favorite quote from each essay.) Prof. McGonigal will collect and share these on the course website. The email address is on The Internet.</quote>

Previously filled.

Roundup of Futures (Studies) Thinking

  • Sight line into the future
  • Futures scenarios
    • It all comes apart
  • Noted that the detente on civil rights and civil society ended with the election of Trump.  By giving voice, or lacking the will to voice the societal expectations, empowered the nutcases.  “The Democrats” had no strong concepts to counter the slience; the center did not hold.
  • stocks vs flows of John Hagel
  • study extremes to triangulate the middle, Foucault [really?]
  • That one about cognitive deficits that I read ona plane.
  • Peter Diamond, Guns, Germs, Steel.
  • Zero consumer service
  • Zero repair
  • Extreme Intellectual Property
    • Every thing embodies Intellectual Property
    • Everything is copyrighted, nothing is “unowned” or “public”
    • Everything is licensed, nothing is sold.
    • Such rights persist in perpetuity.
  • “The Gini Coefficient” of the future
    • The future is here, it is just not evenly distributed.
    • The income is here, it is just not evenly distributed.
  • Solutionism
    Techno-solutionism
  • Wicked problems
  • Quantitative Easing no longer operates
    Inflationism commences; “the reflation”
  • and

Roundup of unnoted & unfilled items for C++ & SCOLD

Within “Major” C++ Components

  • Kyoto Cabinet
  • Kyoto Tycoon
  • lstio (of Google)
  • ros (Robot Operating System)
  • GraphQL, for C++
  • and

Within Demonstration Projects

  • Wall of Sheep
  • iptables emulator (simulator, exhibition)
  • Reboot the Sun Microsystems Network Architecture
    but with json, avro, etc.

    • portmapper
    • xdr
    • the yp thingy?
      feels like LDAP
  • Conversational Request-Response Daemon
    • NNTP
    • SMTP
  • Pig in C++
    • with threading
      • std::future
      • std::promise
    • with UDFs
  • and

Within C++ SCOLD

  • for module-sqlite
    • connect & open
      • sqlite::open::Result
      • the exception contains the filename
    • errors use the std::error_code std::error_category system
      • status::Code
      • std::error:code
    • insert_last_rowid
    • step(…) as an ADL function
    • row(…) and done(…) are not errors
      they are legitimate flavors of success.
    • function as<…>(…)
    • use concepts to guard the return types
      • as<std::string>(…)
      • as<std::string_view>(…)
      • as<c::stringz>(…) and consty
      • as<c::stringn>(…) and consty
  • ideas for top-level namespaces
    • rest
    • more
    • want
    • have
  • exception taxonomization
    • std::length_error
      • for resizing
      • for sizing
      • because it really is a “programmer’s error”
      • because it isn’t a resource exhaustion error (that is something else)
    • std::out_of_range
      • for indexing-type access violations
      • not std::invalid_argument
  • something about an object iterator
    • a:b, c:d
  • rename module-file-slurp → module-slurp
  • slurp::Failure
    • descends from std::ios::failure
    • rethrow ios::failure
  • promote 1-arg copy
    • std::copy(…3-arguments…)
    • want::copy(1-argument)
      • is like std::move(…)
      • can be elided, or must not be elided?
  • for module-sys-time, namespace sys::time
    • from when milliseconds were good enough
    • ftime and timeb
      but is labeled as “deprecated” nowadays
  • for module-sys-posix
    • sleep
    • usleep
    • returns the time remaining
  • The variants of the syscalls with timeouts.
    • run the syscall in a separate thread
  • module-ish
  • the grab bag, the collection
  • recollect module-mvr in there
  • and

Within Cloud

  • C++ MapReduce & Pig thingy, out of some EU Uni.
  • Apache Beam
    • Uses a dataflow-like language specification
    • Runtimes (“Runners”)
      • Java
      • Python
    • beam.apache.org
    • nifi.apache.org
Posted in C++

Roundup of unnoted & unfilled items

Within Adtech

  • Something from Doc Searls
    • in Medium
    • Zingy, ranty.
  • AdChain
  • AdMarket
  • Basic Attention Token (BAT)
  • Distributed Application Organization (DAO)
  • Investment in adtech reduces
    • LUMAscape, ending their funding runways
    • 80% reduction in (new) funding
    • Because of GDPR
    • Because of nonviability
  • Audience Science shut down 30 days after losing Proctor & Gamble
  • Prebid.js

Within Architecture

  • Architecture (talks) at InfoQ
    • Criteo
    • Spotify

Within C++

Separately noted.

Within Futures (Studies) Thinking

Separately noted.

Within Surveillance (Studies)

  • The Ultrasound Tracking Ecosystem
    • Vigna
    • PETS 2015
    • PETS 2016
    • UB.easec.org
  • and

Webware

Enbrowser all the things; JavaScript wrapup all the primitives.

  • WebAssembly
    • in lieu of Java bytecode.
    • in lieu of x86_64 codes.
  • WebBluetooth
  • WebUSB
    • USB 3.0
    • NIC
  • and

What Future Studies Is, And Is Not | Jim Dator

Jim Dator (U. Hawaii); What Future Studies Is, And Is Not; WHEN? 2 pages ← whatfuturestudiesis

Mentions

  • ideas about the future.
  • images about the future.
  • envisioning the futures
  • alternative futures.
  • several conflicting images at one time

Approach

  • as predictive science → fortune telling (ahem, shame on you)
  • as anticipation → as prudence & reasonableness.

Dator’s Laws of the Future

  1. “The future” cannot be “predicted” because “the future” does not exist.
    1. While “the future” cannot be “predicted,”
      yet “alternative futures” can and should be “forecast.”
    2. “The future” cannot be “predicted,”
      but “preferred futures” can and should be

      • envisioned,
      • invented,
      • implemented,
      • evaluated,
      • revised,
      • … and other verbs.
      • rinse & repeat.
    3. Futures Studies precedes, then linked to
      • Strategic Planning,
        and thence to
      • Administration (Execution).
  2. Any useful idea about the futures should appear to be ridiculous.
    1. Because new technologies permit new behaviors and values,
    2. “The most likely future” is often one of the least likely futures.
    3. To be useful, the theoretician’spractitioner’s ideas should expect to be ridiculed and the ideas rejected (initially).
    4. The practitioner must defend the implausible condepts proposed (that’s the job).
  3. “We shape our tools and thereafter our tools shape us.”

Methods & Frameworks

  • long wave (theory)
  • cyclical forces (theory)
  • generations (theory)
    the “generations” through their life cycles (age-cohort analysis)

Verbs

  • forecasting
  • envisioning
  • creating

but definitely not predicting

Quotes

  • “We shape our tools and thereafter our tools shape us,” attributed to Marshall McLuhan.

Referenced

  • Wendell Bell, Foundations of Futures Studies. Transaction Publishers, 1997. Two Volumes.
    • Foundations of Futures Studies: Volume 1: History, Purposes, and Knowledge; Routledge; 2003-08-31; 404 pages; Amazon:0765805391: paper: $32+SHT.
    • Foundations of Futures Studies: Volume 2: Values, Objectivity, and the Good Society; Routledge; 2004-03-31; 404 pages; Amazon:0765805669: paper: $32+SHT.
  • Jim Dator, Advancing Futures: Futures Studies in Higher Education. Praeger, 2002-04-30; 408 pages; Amazon:0275976327: paper: $36+SHT.

Institutions

The Future as a Way of Life: Alvin Toffler’s Unfinished Business | Marina Gorbis

Marina Gorbis (IFTF); The Future as a Way of Life: Alvin Toffler’s Unfinished Business; In Her Blog on Medium; 2016-07-14 ← thefutureisawayoflife

Marina Gorbis

Mentions

  • midway into the piece, a generalized a apology towards class structure & privilege.
    <quote>yep, most were white men and the field is still heavily dominated by them</quote>
  • Office of Technology Assessment, closed in 1995
  • Toffler’s proposal
    Foresight Department led by a Secretary of the Future
    <ahem>sounds very Central Planning oriented</ahem>
  • . Institute for the Future
    • 1977, a workshop, gun control (or its inverse)
  • Tessa Finlev
  • Silicon Valley Culture
    • Uber
  • East Coast progressive culture
    • Platform Cooperativism movement
  • Institute for the Future (IFTF)
  • Affect
    • awe
    • awe → expanded perception of time (when “time stood still”)
    • Urgent Optimizm, attributed to Jane McGonigal,
    • volunteerism
  • T-shaped people
    • Deep in some area
    • Broad (but shallow) in the rest
  • Forecasters (are, do)
    • historians
    • sensemaking
      (the “sense-making” of Dervin, Russell, Stefick, Pirolli, Card, Weick?)
  • Futures Thinking
    • Design Thinking
    • Systems Thinking

Quotes

  • “Future Shock,” due to Alvin Toffler
  • “The future is already here — it’s just not evenly distributed,”  attributed to William Gibson
  • ergo
    The future shock is already here — it’s just not evenly distributed,”  attributed to Marina Gorbis
  • “The best way to predict the future is to invent it,” attributed Alan Kay.

Who

  • Paul Baran(deceased 2011-03-26), credited as a “futurist”
    • RAND Corporation
    • ARPA
    • Co-founder, Institute for the Future (IFTF)
  • Chuck Darrah
  • Douglas Engelbart (deceased 2013-07-03), credited as a “futurist”
  • Jan English-Lueck
  • Tessa Finlev, IFTF
  • William Gibson
  • Marina Gorbis
  • Olaf Helmer(deceased, 2011-04-11), credited as a “futurist”
    • RAND Corporation
    • DELPHI forecasting
    • Co-founder, Institute for the Future (IFTF)
    • Pressor of Futuristics, B.School, USC
  • Alan Kay
  • Kevin Kelly
    • founding executive editor, WIRED
    • co-founder of The Long Now Foundation.
    • The Inevitable
    • Book output back into 1994.
  • Jane McGonigal, IFTF
    • game researcher
    • game designer
    • aphorism “Urgent Optimism”
  • Marshall McLuhan
  • Howard Rheingold
  • Alvin Toffler (deceased, 2016-06-27).
  • Nicolas Weidinger, IFTF

Argot

  • signals → <quote>everyday examples of the future in the present and they are everywhere in Silicon Valley</quote>
  • socialstructing → a neologism of Marina Gorbis (i.e. social structuring).

Referenced

  • Jan English-Lueck, Chuck Darrah; Cultures@SiliconValley; Stanford University Press
    • Second Edition; 2017-08-23; 256 pages; Amazon:1503600238: $85.00 (preorder only)
    • First Edition; 2002-03-29; 201 pages; Amazon:0804744297; paper: $5+SHT.
  • Marina Gorbis; Nature of the Future; Free Press; 2013-04-09; 256 pages; Amazon:1451641184: Kindle: $14, paper: $6+SHT.
  • Kevin Kelly; The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future; Viking (1st edition), Penguin Books; 2016-06-07; 336 pages; Amazon:0143110373: Kindle: $14, paper: $7+SHT.
  • Alvin Toffler
  • Nicholas Weidinger (IFTF), “Futures Thinking is the New Design Thinking,” In Their Blog, 2014-03-14

Salad

  • social media
  • “software eats” (people’s jobs)
  • loosely organized (networks of …)
  • loss of trust (in institutions)
  • makers of the future
  • Silicon Valley
  • startup
  • founder
  • incubator
  • research outfit
  • venture capitalist
  • vision
  • bubble
  • revolutionary transformation
  • disruption
  • exponential
  • social media
  • Peacebuilding
  • Platform Cooperativism (movement) is East Coast thinking
  • Uber
  • East Coast (contra West Coast)
  • communities
  • directions of technologies
  • progressive thinkiers (activists)
  • signals
  • Google
  • self-driving car
  • awe
  • expanded perception of time
  • volunteerism
  • choosing experiences over material objects
  • life satisfaction
  • Urgent Optimism, of McGonigal
  • media theory (meda theorist, Howard Rheingold)
  • IBM
  • T-shaped people

Future Studies (partial) Roundup through 2017-06

Concepts

  • AI <ahem>1970→2004, AI defined as that which computers could not do (FAIL); 2004→2017 AI redefined to that which computers can do (success!)</ahem>
  • Big Data (which is so very big, we are all very impressed down here)
  • The Coming War on General Purpose Computing [Doctorow]
  • Communities of Practice
  • Computers are very delicate
  • Creative Class (Florida)
  • Data (Personal Data) is the New Oil → Oil is the new Oil.
  • Designed in California, Manufatured in China
  • The Disappearing Computer
  • A Failure of Imagination
  • A Failure to Launch
  • Financialization
  • Free Culture → Code is Law (Lessig)
  • Entitlement → Implicit entitlement of The Millennials & The Homelanders (Generation Z)
  • Internet of Thingies (IoT)
    Internet of Unpatchable Toys (IoT)
    Internet of Abandoned Devices
  • Internet Trends → Mary Meeker, the boosterism
  • It Was Ever Thus → plus ça change, plus c’est la même chose
  • Hollowing Out → There is no silicon in Silicon Valley. and there and hasn’t been for over a generation.
  • Innovation Duration
  • Platform Cooperativism
  • Post Open Source (POSS)
  • Regulation → public-private “partnerships,” “government has a role to play,” GDPR
  • Regulation → Net Neutrality
  • Regulation → Internet of Thingies
  • Robots → <gee-whizz!>Drones, Servos, Driverlessness</gee-whizz!>
  • Silicon Valley Culture → techno optimism, techno determinism, against Washington.
  • Stagnation → The Great Stagnation, The Downward Ramp
  • STEM, STEEP → labor shortage, labor economics, immigration
  • Stocks vs Flows (Hagel’s concepts)
  • Surveillance Studies (and the journal)
  • Trust, But Verify
  • West Coast Culture
  • Universal Basic Income (UBI) → To each according to their need.

Exemplars

Trilemma of The Global Scenarios 2025 of Shell Oil (2005)

  • Three Poles
    • Security, Coercion, Regulation
    • Efficiency, Market Incentives
    • Social Cohesion, Justice, Communitarianism (The force of community)
  • Scenarios
    • Low Trust Globalization
    • Flags
    • Open Doors

Separately noted.

Quadratic (ahem, a “Quadrilemma”) of the Internet Futures Scenarios

  • Two Axis, Four Quadrant
  • X-Axis
    • Command & Control
    • Decentralized & Distributed
  • Y-Axis
    • Generative
    • Reductive
  • Scenarios
    • Porous Garden
    • Moats and Drawbridges
    • Common Pool
    • Boutique Networks

Separately noted.

Theories

In Jimi Wales’ Wiki, Encyclopedia Brittanica, etc.

Popularization & Practice

  • Clayton Christianson
    • Disruptive Innovation
    • Innovation to Predict Change
  • Geoffrey Moore.
    • Core vs Context,
    • Chasm,
    • Tornado,
    • Gorilla etc.

Referenced

Noted herein

Categorical

Background

  • Counterfactual Histories, the method of counterfactuals
  • Covering-Law Model
  • Post Open Source (POSS); In Jimi Wales’ Wiki.
  • Ben Ramsey; Post-Open Source; In His Blog; 2016-004-26.
  • Quadrilemma, see Trilemma, see Dilemma
  • Trilemma, In Jimi Wales’ Wiki
    also: uneasy triangle, impossible trinity, and other branded concepts

    • stable price level, full employment, free collective bargaining
    • impossible trinity,
    • fair, free, equal,
    • democracy, national sovereignty, global economic integration

Back References

Tom Insel is “The Smartphone Psychiatrist” at Mindstrong Health | The Atlantic

Tom Insel is “The Smartphone Psychiatrist” promoting his employer ‘Mindstrong’;
David Dobbs; In The Atlantic; 2017-07.

tl;dr → a promotion of Mindstrong Health, announcing $14M in funding today
tl;dr → a hagiogaphy of Dr. Thomas Insel, its public face.

Occasion

Mindstrong Health Raises $14 Million in Series-A Funding; press release; 2017-06-15.
Teaser: Founding team includes the former Director of the National Institute of Mental Health, Dr. Tom Insel, and former Director of the National Cancer Institute, Dr. Richard Klausner

Tom Insel
  • Mindstrong, startup, Palo Alto, CA
  • Product Manager (Director?), Verily (the ‘V’ in the Alphabet pantheon as Google’s “health” hobby).
  • (ex-)National Institute of Mental Health (NIMH).
  • other institutions in the article.
Mindstrong Health

See below

Mentions

  • Diagnostic and Statistical Manual of Mental Disorders (DSM)
  • Verily of Google
    Mountain View, CA
  • Tom Insel
    • one of four brothers
    • curriculum vitae in the article
    • Pleasanton, CA
  • H. Herbert Insel
    • father of Tom Insel
    • an eye surgeon
    • Dayton, OH
  • clomipramine
  • OCD
  • prairie-vole
  • Insel, Wang, and Young
  • biology vs environment, teach the controversy (nature vs nurture)
  • Thomas Insel; Towards a New Understanding of Mental Illness; TED Talk, 2013.

Promotion

<quote>The force they hope to harness is the power of daily behavior, trackable through smartphone use, to reflect one’s mental health. As people start to slide into depression, for instance, they may do several of the following things easily sensed by a phone’s microphones, accelerometers, GPS units, and keyboards: They may talk with fewer people; and when they talk, they may speak more slowly, say less, and use clumsier sentences and a smaller vocabulary. They may return fewer calls, texts, emails, Twitter direct messages, and Facebook messages. They may pick up the phone more slowly, if they pick up at all, and they may spend more time at home and go fewer places. They may sleep differently. Someone slipping toward a psychotic state might show similar signs, as well as particular changes in syntax, speech rhythm, and movement.</quote>

Snide

<quote>Psychiatry has always struggled to be taken seriously as a science. By the 1980s, the field seemed especially lost. Its best drugs were from the 1950s and ’60s. Most of its hospitals, their failings made infamous by works such as Sylvia Plath’s The Bell Jar and Ken Kesey’s One Flew Over the Cuckoo’s Nest, had been closed. Talk therapy, which often works, but by psychobiological pathways painfully difficult to discern, was frequently lampooned. For these and other reasons, including its penchant for savage infighting, psychiatry in the ’70s was “a collection of diverse cults rather than a medical science,” as Melvin Sabshin, a onetime medical director of the American Psychiatric Association, later put it. </quote>

<quote>A therapist, the joke goes, knows in great detail how a patient is doing every Thursday at 3 o’clock.>/quote>

Background

Theory

the two components necessary to any approach to mental-health care—assessment

  1. collection and analysis of “data”
    • self-attested by the patient
    • logged by the phone
  2. intervention
    • informal social
    • medical support, inpatient
    • medical support, outpatient

prime, an app

  • prime → (Personalized Real-time Intervention for Motivation Enhancement
  • Danielle Schlosser
    • a clinical psychologist
    • recruited to Verily from the psychiatry department at UC San Francisco by Thomas Insel
    • developed prime. a monitoring app, for an outpatient’s phone
  • Concept
    Social proof to the cohort that they are all “normal” people who are able to “function.”
  • Applicable
    • people ages 14 to 30
    • recently diagnosed with schizophrenia
  • Feature-Function
    <paraphrase>

    1. modeled on Facebook
      i.e. a circle of ‘friends’
    2. connecting people so they can turn to one another for help, perspective, and affirmation.
    3. reading material → set of motivational essays, talks, and interactive modules
      [which] guide with decisions and review dilemmas common among the membership.
    4. monitoring & alerting → spotting emerging crises and responding with peer, social-service, and clinician support.

</paraphrase>

Mindstrong Health

  • co-founders
    • Richard Klausner
    • Paul Dagum
    • Michael Friberg
  • Palo Alto
  • something about 2017-05, probably the date of the interview for the article
  • Roles
    • Insel → expertise and connections in the mental-health field
    • Klausner → business
    • Dagum → data-analysis

Statement

<quote ref=”presser>Based in Palo Alto, California, Mindstrong’s patented science and technology was developed by Dr. Dagum, and is based on four years of extensive clinical studies applying machine intelligence to human-computer interactions patterns. Mindstrong products are in clinical trials in numerous partnership projects with payers, providers, academics and the pharmaceutical industry to bring these new tools to bear on answering the most fundamental questions in behavioral health. Its Board of Directors includes Richard Klausner, MD, Jim Tananbaum, MD, Robert Epstein, MD, Thomas Insel, MD, and Paul Dagum, MD PhD.</quote>

Concept

  • Mindstrong does assessment.
  • Mindstrong does “learning-based mental-health care.”
  • Mindstrong does continuous assessment and feedback [which] would drive the interventions.
  • Mindstrong does measurement-based practices [would be for] all therapies

<quote>Smartphones can track daily behaviors that reflect mental health. A phone can sense the beginning of a crisis and trigger an appropriate treatment response. This idea has been floating around Silicon Valley and mental-health circles for several years. Insel estimates that a good five or 10 other companies or research teams—including Verily—are trying to do something similar. Mindstrong hopes to gain an edge by combining Insel’s expertise and connections in the mental-health field with Klausner’s business experience and Dagum’s data-analysis tools and skills—and by moving quickly.</quote>

Plan

  • 2018 & 2019 → testing phone-based data-collection-and-analysis systems,
  • 2019 & 2020 → explore ways to partner with others to provide intervention.

Intellectual Property

three patents for a data-collection-and-analysis system for such purposes.
Paul Dagum designed this system [is a named inventor?]

Checkboxes

  • Mindstrong will collect information
  • Mindstrong will use an opt-in
  • Mindstrong will use encryption
    <quote>all data will be strongly encrypted</quote>
  • Mindstrong will use HIPPA<quote>All data will be firewalled according to strict patient-privacy practices.</quote>
  • Mindstrong will only store metadata
    • not
      • voice
      • typed
    • e.g.
      • semantic structures
      • repeated use of key words or phrases
      • estimated
      • emotional state
      • cognitive states,
        e.g.

        • depression,
        • mania,
        • psychosis,
        • cognitive confusion.

Competition

Verily (Google)

  • Andy Conrad, CEO
  • <quote>a 500-person company (Verily>part of a 74,000-person company (Alphabet)
  • South San Francisco

7 Cups

  • Has an app.
  • Another private venture.
  • Glen Moriarty, CEO
  • Insels daughter NAME is an employee.
  • Demographic
    • young
    • diverse
    • 90% are under the age of 35
    • “likely to go underserved by traditional mental-health care.”
  • Applies DASS‑21Anonymizes the results.
Concept

<quote>7 Cups provides text-based peer counseling and support for people with depression or anxiety or a long list of other conditions. Registering for the simpler services, such as peer connection, takes only seconds, and users can also get referrals to either coaches or licensed mental-health counselors and psychologists.</quote>

DASS‑21

DASS-21 → Depression Anxiety Stress Scales

DASS, University of New South Wales, AU

There is a manual

Questions

Separately filled.

De-Anonymizing Web Browsing Data with Social Networks | Su, Shukla, Goel, Narayanan

Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan; De-Anonymizing Web Browsing Data with Social Networks; draft; In Some Venue Surely (they will publish this somewhere, it is so very nicely formatted); 2017-05; 9 pages.

Abstract

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world e↵ectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest-scale demonstrated de-anonymization to date.

Mentions

yes.

Quotes

  • <quote>Network adversaries—including government surveillance agencies, Internet service providers, and co↵ee shop eavesdroppers—also see URLs of unencrypted web traffic. The adversary may also be a cross-device tracking company aiming to link two di↵erent browsing histories (e.g., histories generated by the same user on di↵erent devices). For such an adversary, linking to social media profiles is a stepping stone.</quote>

Headline

374 people confirmed the accuracy of our deanonymization attempt.
268 people (72%) were the top candidate generated by the MLE when using t.co links.
303 people (81%) were among the top 15 candidates generated by the MLE when using t.co links.
Yet only 49% de-anonymization when using fully expanded links (the redirect target of the t.co link)
Background

<paraphrasing>We recruited participants by advertising the experiment on a variety of websites, including

  • Twitter,
  • Facebook,
  • Quora,
  • Hacker News,
  • Freedom to Tinker
Story Line
649
people submitted web browsing histories.
119 cases (18%)
the application encountered a fatal error (e.g., because the Twitter API was temporarily unavailable), and it was unable to run the de-anonymization algorithm.
530 cases
remaining are useful.

87 users (16%)
had fewer than four informative links, and so no attempt to de-anonymize them was made.
443 users
remaining are useful.

374 users (84%)
confirmed whether or not our de-anonymization attempt was successful.
77 users (21%),/dt>
additionally disclosed their identity by signing into Twitter.

Apology: noted that the users who participated in our experiment are not representative of the Twitter population. In particular, they are quite active: the users who reported their identity had a median number of 378 followers and posted a median number of 2,041 total tweets.

</paraphrasing>

Framing (Environment)

  • TargetConsumer is a Registered Twitter User,
    with activity and warm content selection algo in operation at Twitter HQ
  • Twitter algo selects snippets for presentation to TargetConsumer.
  • TargetConsumer either elects to read or discards the linked page.
  • An URL trail is recorded by The Panopticon Surveillance Machinery in The Record
  • Adversary has access to The Record across long spans of time and large numbers of TargetConsumers.

Problem Statement

  • Can one or many TargetConsumers be distinguished solely by URL traces in The Record?

Algorithm (Conceptual)

See C. Y. Ma, D. K. Yau, N. K. Yip, N. S. Rao. “Privacy vulnerability of published anonymous mobility traces,” In IEEE/ACM Transactions on Networking, 21(3):720–733, 2013.
<paraphrasing>

  1. The simple model of web browsing behavior in which a user’s likelihood of visiting a URL is governed by the URL’s overall popularity and whether the URL appeared in the TargetConsumer’s Twitter feed.
  2. For each TargetConsumer, we compute their likelihood (under the model) of generating a given anonymous browsing history.
  3. Identify the TargetConsumer most likely to have generated that history.

</paraphrasing>

Argot

  • Cookie Syncing
  • E-Tag
  • HTML5 localStorage
  • HTTP (HTTP)
  • Jaccard Similarity
  • Maximum Liklihood Estimate (MLE)
  • URL (URL)

Promotions

  • Ad Networks Can Personally Identify Web Users; Wendy Davis; In MediaPost; 2017-01-20.
    <quote> The authors tested their theory by recruiting 400 people who allowed their Web browsing histories to be tracked, and then comparing the sites they visited to sites mentioned in Twitter accounts they followed. The researchers say they were able to use that method to identify more than 70% of the volunteers.</quote>

References

  • G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, C. Diaz. The web never forgets: Persistent tracking mechanisms in the wild. In Proceedings of ACM Conference on Computer Communications & Security (CCS), pages 674–689. ACM, 2014.
  • G. Acar, M. Juarez, N. Nikiforakis, C. Diaz, S. Gürses, F. Piessens, B. Preneel. Fpdetective: dusting the web for fingerprinters. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security (CCS), pages 1129–1140. ACM, 2013.
  • M. D. Ayenson, D. J. Wambach, A. Soltani, N. Good, C. J. Hoofnagle. Flash cookies and privacy II: Now with HTML5 and ETag respawning. 2011.
  • C. Budak, S. Goel, J. Rao, G. Zervas. Understanding emerging threats to online advertising. In Proceedings of the ACM Conference on Economics and Computation, 2016.
  • M. Chew, S. Stamm. Contextual identity: Freedom to be all your selves. In Proceedings of the Workshop on Web,/em>, volume 2. Citeseer, 2013.
  • ] N. Christin, S. S. Yanagihara, K. Kamataki. Dissecting one click frauds. In Proceedings of the 17th ACM conference on Computer and Communications Security
  • Y.-A. De Montjoye, C. A. Hidalgo, M. Verleysen, V. D. Blondel. Unique in the crowd: The privacy bounds of human mobility. In Scientific Reports, 3, 2013.
  • Y.-A. De Montjoye, L. Radaelli, V. K. Singh, et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. In Science, 347(6221), 2015.
  • P. Eckersley. How unique is your web browser? In, pages 1–18. Springer, 2010.
  • S. Englehardt, A. Narayanan. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2016.
  • S. Englehardt, D. Reisman, C. Eubank, P. Zimmerman, J. Mayer, A. Narayanan, E. W. Felten. Cookies that give you away: The surveillance implications of web tracking. In Proceedings of the 24th Conference on World Wide Web (WWW), 2015.
  • Ú. Erlingsson, V. Pihur, A. Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the Conference on Computer and Communications Security (CCS), 2014.
  • D. Fifield, S. Egelman. Fingerprinting web users through font metrics. In Proceedings of the International Conference on Financial Cryptography and Data Security, 2015.
  • S. Hill, F. Provost. The myth of the double-blind review?: Author identification using only citations. In SIGKDD Explor(ification) Newsletter, 5(2):179–184, Dec. 2003.
  • M. Korayem, D. J. Crandall. De-anonymizing users across heterogeneous social computing platforms. In Proceedings of the Internation Conference on W(something) S(something) M(something) as “Some Acronym” (ICWSM), 2013.
  • A. Korolova, K. Kenthapadi, N. Mishra, A. Ntoulas. Releasing search queries and clicks privately. In Proceedings of the 18th International Conference on World Wide Web (WWW). ACM, 2009.
  • B. Krishnamurthy, K. Naryshkin, C. Wills. Privacy leakage vs. protection measures: the growing disconnect. In Proceedings of the Web
  • B. Krishnamurthy, C. E. Wills. On the leakage of personally identifiable information via online social networks. In Proceedings of the 2nd ACM Workshop on Online Social Networks (WOSN), pages 7–12. ACM, 2009.
  • P. Laperdrix, W. Rudametkin, B. Baudry. Beauty and the beast: Diverting modern web browsers to build unique browser fingerprints. In Proceedings of the 37th IEEE Symposium on Security and Privacy, 2016.
  • A. Lerner, A. K. Simpson, T. Kohno, F. Roesner. Internet jones and the raiders of the lost trackers: An archaeological study of web tracking from 1996 to 2016. In Proceedings of the 25th USENIX Security Symposium, 2016.
  • T. Libert. Exposing the invisible web: An analysis of third-party http requests on 1 million websites. In International Journal of Communication, 9:18, 2015.
  • C. Y. Ma, D. K. Yau, N. K. Yip, N. S. Rao. Privacy vulnerability of published anonymous mobility traces. In IEEE/ACM Transactions on Networking, 21(3):720–733, 2013.
  • A. Marthews, C. Tucker. Government surveillance and internet search behavior. Available at ssrn:2412564, 2015.
  • N. Mathewson, R. Dingledine. Practical traffic analysis: Extending and resisting statistical disclosure. In Proceedings of the International Workshop on Privacy Enhancing Technologies (PETS), pages 17–34. Springer, 2004.
  • J. R. Mayer, J. C. Mitchell. Third-party web tracking: Policy and technology. In Proceedings of the 2012 IEEE Symposium on Security and Privacy. IEEE, 2012.
  • K. Mowery, H. Shacham. Pixel perfect: Fingerprinting canvas in HTML5. In Proceedings of the Conference with the Acronym “W2SP” (W2SP), 2012.
  • A. Narayanan, H. Paskov, N. Z. Gong, J. Bethencourt, E. Stefanov, E. C. R. Shin, D. Song. On the feasibility of internet-scale author identification. In Proceedings of the IEEE Symposium on Security and Privacy, 2012.
  • A. Narayanan, V. Shmatikov. Robust de-anonymization of large sparse datasets. In Proceedings of the 2008 IEEE Symposium on Security and Privacy (SP), pages 111–125. IEEE, 2008.
  • N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, G. Vigna. Cookieless monster: Exploring the ecosystem of web-based device fingerprinting. In Proceedings of the 2013 IEEE symposium on Security and Privacy (SP), pages 541–555. IEEE, 2013.
  • L. Olejnik, G. Acar, C. Castelluccia, C. Diaz. The leaking battery A privacy analysis of the HTML5 Battery Status API. Technical Report, WHERE? 2015.
  • L. Olejnik, C. Castelluccia, A. Janc. Why Johnny can’t browse in peace: On the uniqueness of web browsing history patterns. In Proceedings of the 5th Workshop on Hot Topics in Privacy Enhancing Technologies (PETS), 2012.
  • J. Penney. Chilling effects: Online surveillance and wikipedia use. In Berkeley Technology Law Journal, 2016.
  • A. Ramachandran, Y. Kim, A. Chaintreau. “I knew they clicked when I saw them with their friends”. In Proceedings of the 2nd Conference on Online Social Networks, 2014.
  • F. Roesner, T. Kohno, D. Wetherall. Detecting and defending against third-party tracking on the web. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, pages 12–12. USENIX Association, 2012.
  • K. Sharad, G. Danezis. An automated social graph de-anonymization technique. In Proceedings of the 13th Workshop on Privacy in the Electronic Society (WPES), pages 47–58. ACM, 2014.
  • A. Soltani, S. Canty, Q. Mayo, L. Thomas, C. J. Hoofnagle. Flash cookies and privacy. In Proceedings of the AAAI Spring Symposium: Intelligent Information Privacy Management, volume 2010, pages 158–163, 2010.
  • J. Su, A. Sharma, S. Goel. The effect of recommendations on network structure. In Proceedings of the 25th Conference on World Wide Web (WWW), 2016.
  • G. Wondracek, T. Holz, E. Kirda, C. Kruegel. A practical attack to de-anonymize social network users. In Proceedings of the IEEE Symposium on Security and Privacy, 2010.

Previously filled.

The Death of Rules and Standards | Casey, Niblett

Anthony J. Casey, Anthony Niblett; The Death of Rules and Standards; Coase-Sandor Working Paper Series in Law and Economics No. 738; Law School, University of Chicago; 2015; 58 pages; landing, copy, ssrn:2693826.

tl;dr → because reasons and
  • Prediction Technologies
  • Communication Technologies

Abstract

Scholars have examined the lawmakers’ choice between rules and standards for decades. This paper, however, explores the possibility of a new form of law that renders that choice unnecessary. Advances in technology (such as big data and artificial intelligence) will give rise to this new form – the micro-directive – which will provide the benefits of both rules and standards without the costs of either.

Lawmakers will be able to use predictive and communication technologies to enact complex legislative goals that are translated by machines into a vast catalog of simple commands for all possible scenarios. When an individual citizen faces a legal choice, the machine will select from the catalog and communicate to that individual the precise context-specific command (the micro-directive) necessary for compliance. In this way, law will be able to adapt to a wide array of situations and direct precise citizen behavior without further legislative or judicial action. A micro-directive, like a rule, provides a clear instruction to a citizen on how to comply with the law. But, like a standard, a micro-directive is tailored to and adapts to each and every context.

While predictive technologies such as big data have already introduced a trend toward personalized default rules, in this paper we suggest that this is only a small part of a larger trend toward context- specific laws that can adapt to any situation. As that trend continues, the fundamental cost trade-off between rules and standards will disappear, changing the way society structures and thinks about law.

Table of Contents

  1. Introduction
  2. The Emergence Of Micro-Directives And The Decline Of Rules And Standards
    1. Background: Rules and standards
    2. Technology will facilitate the emergence of micro-directives as a new form of law
    3. Demonstrative examples
      • Example 1: Predictive technology in medical diagnosis
      • Example 2: Communication technology in traffic laws
    4. The different channels leading to the death of rules and standards
      1. The production of micro-directives by non-legislative lawmakers
      2. An alternative path: Private use of technology by regulated actors
  3. Feasibility
    1. The feasibility of predictive technology
      1. The power of predictive technology
      2. Predictive technology will displace human discretion
    2. The feasibility of communication technology
  4. Implications And Consequences
    1. The death of judging? Institutional changes to the legal system
    2. The development and substance of policy objectives
    3. Changes to the practice of law
    4. The broader consequences of these technologies on individuals
      1. Privacy
      2. Autonomy
      3. Ethics
  5. Conclusion

Snide

  • heavy-handed use of the metaphor “death X” in lieu of the more mundate “cessation of use of the technique X.”
  • At least they didn’t use the metaphors of “sea change” or “tectonic shifts” from the respective fields of weather prediction or geology.
  • <GEE-WHIZZ!>As economist Professor William Nordhaus notes, the increase in computer power over the course of the twentieth century was “phenomenal,”</GEE-WHIZZ!!>

Mentions

  • catalog of personalized laws.
    “special law for you.”
  • rules and standards
    contra
    captures and benefits
  • micro-benefits
  • predictive technology
  • uncertainty of law
  • <quote>The legislature merely states its goal. Machines then design the law as a vast catalog of context-specific rules to optimize that goal. From this catalog, a specific micro-directive is selected and communicated to a particular driver (perhaps on a dashboard display) as a precise speed for the specific conditions she
    faces.</quote>
  • positive versus normative (analysis)
  • (legislative) decision-making has
    • errors
    • costs
  • (subject) compliance has
    • cost
    • uncertainty
  • There are economies of scale in compliance
    • frequency of event
    • diversity of events
  • Conceptualize the frequency of the regulated event relative the specificity of the regulation.
  • The Combination
    • Prediction Technologies
    • Communication Technologies
  • <quote>The wise draftsman . . . asks himself, how many of the details of this settlement ought to be postponed to another day, when the decisions can be more wisely and efficiently and perhaps more readily made?</quote>, attributed to Henry Hart, Albert Sacks.
  • Claim
    • Standards are flexible, broad but uncertain in adjudication;
      so service delivery is tailored
      therefore the salubrious effect obtains.
    • Rules are specific, narrow but certain in adjudication;
      so service delivery is pre-specified, constrained
      therefore mis-applications occur.
    • Technology (cited) removes the distinction between Rules and Standards
  • advance (tax) rulings
  • Private Letter Rulings, IRS
  • No Action Letter, SEC

Argot

  • one size fits all
  • bright-line rule
  • over-inclusive (contra under-inclusive)
  • optimal decision rule
  • reasonable care
  • Error Typology(in hypothesis testing)
    • Type I Error
    • Type II Error
  • health surveillance technologies
  • second-order regulation

References

Sure, it’s a legal-style paper so there are 191 footnotes sprinkled liberally throughout the piece.  Only selected references were developed.

Followup

  • <quote>Indeed, some suggest that Moore’s Law is akin to a self-fulfilling prophecy.</quote>
    Harro van Lente & Arie Rip, “Expectations in Technological Developments: an Example of Prospective Structures to be Filled in by Agency”  researchgate, In Getting New Technologies Together: Studies In Making Sociotechnical Order, 206 (Cornelis Disco & Barend van der Meulen, eds. 1998), Amazon:311015630X: paper: $210+SHT.
  • …and more…

Via: backfill.

The Shell Global Scenarios to 2025 | Shell Oil


Product

Summary

Low-Trust Globalization

  • prove it → trust, but verify
  • culture of blame
  • lawyers, accountants
  • reactive
  • compliance
  • multii-national corporations
  • bond financing is popular
  • absence of market solutions
  • arbitrage of jurisdiction, regulation.
  • checks & controls

Flags

  • dogmatic about codes and causes
  • interconnected
  • identity culture
  • fragmented, polarized
  • state capture for (ethnic) ends
  • security by isolation
  • national champions
  • high military

Open Doors

  • pragmatic
  • proactive
  • cooperative
  • precautionary principle
  • government in the background
  • reputation, networking skills
  • best-practice codes, self-regulation
  • innovation
  • low duration
  • stable large money chases smaller returns

Signposts (of the Poles)

Security, Coercion, Regulation

Security and trust through coercion and regulation

  1. Terror threat grows
  2. States struggle to find common response to nuclear proliferation.
  3. NATO expansion fuels security concerns
  4. Market regulations place heavy burdens on small businesses

Efficiency and Market Incentives

Efficiency and growth through markets and private initiatives

  1. Business environment improving
  2. China spurs growth
  3. Immigration and liberalization of labor flows
  4. Foreign listings and liberalization of international capital

Social Cohesion, Justice, Communitarianism

Social cohesion through force of community.

  1. Blow to world trade
  2. Resource nationalism on the rise
  3. Social integration, exclusion and identity politics in Europe
  4. Proud to be Japanese

Big Data, Psychological Profiling and the Future of Digital Marketing | Sandra Matz

Sandra Matz; Digital Psychometrics and its Future Effects on Technology; 34 slides.

Talks

  • Sandra Matz; Digital Psychometrics and its Future Effects on Technology; Keynote at ApacheCon; 2017-05-16; video: 23:08.
  • Sandra Matz; Big Data, Psychological Profiling and the Future of Digital Marketing; President’s Lecture, at The Berlin School; On YouTube; 2017-02-20; video: 1:10:52.

Mentions

  • www.sandramatz.com
  • www.psychometrics.cam.ac.uk
  • www.discovermyprofile.com
  • Cambridge Analytica
  • Apply Magic Sauce, Prediction API
  • myPersonality Project
    • myPersonality Database

Psychometrics

  • Personality (Big Five, OCEAN)
  • Values
  • Life Satisfaction
  • Impulsivity
Personality
  • Openness to experience
  • Conscientiousness
  • Extraversion
  • Agreeableness
  • Neuroticism

Sources

Background

Actualities

Referenced

Is Facebook Targeting Ads at Sad Teens?

      ;

Michael Reilly

      ; In

MIT Technology Review

      ; 2017-05-01.
      Teaser:

The social network appears to leverage sensitive user data to aim ads at teenagers who say they feel “anxious” and “worthless.”

Looking into the Internet’s Future, Scenarios | Internet Society

Future Internet at the Internet Society.

Scenarios of 2009

Soliciation 2016→2017

Solicitation of input towards a new output revision. Expected 2016-Q4 through 2017-Q2.

Perspectives

Drivers & Areas

Internet Society
1775 Wiehle Avenue, Suite 201, Reston, VA 20190-5108 U.S.A. +1-703-439-2120
Galerie Jean-Malbuisson 15, CH-1204 Geneva, Switzerland +41 22 807 1444

 

Omega2 by Onion

Availability

variously …

Documentation


Omega2 Project Book

MediaTek

Imagination

Promotions

Online Privacy and ISPs | Institute for Information Security & Privacy, Georgia Tech

Peter Swire, Justin Hennings, Alana Kirkland; Online Privacy and ISPs; a whitepaper; Institute for Information Security & Privacy, Georgia Tech; 2016-05; 131 pages.
Teaser: ISP Access to Consumer Data is Limited and Often Less than Access by Others

Authors
  • Peter Swire
    • Associate Director,
      The Institute for Information
      Security & Privacy at Georgia Tech
    • Huang Professor of Law,
      Georgia Tech Scheller College of Business
      Senior Counsel, Alston & Bird LLP
  • Justin Hemmings,
    • Research Associate,
      Georgia Tech Scheller College of Business
    • Policy Analyst
      Alston & Bird LLP
  • Alana Kirkland
    • Associate Attorney, Alston & Bird LLP

tl;dr → ISP < Media; ISPs are not omnipotent; ISPs see less than you think; Consumer visibility is mitigated by allowed usage patterns: cross-ISP, cross-device, VPN, DNS obfuscation, encryption.  Anyway, Facebook has it all and more.

Consumer profiling observation is already occurring by other means anyway.

<quote> In summary, based on a factual analysis of today’s Internet ecosystem in the United States, ISPs have neither comprehensive nor unique access to information about users’ online activity. Rather, the most commercially valuable information about online users, which can be used for targeted advertising and other purposes, is coming from other contexts. Market leaders are combining these contexts for insight into a wide range of activity on each device and across devices. </quote>

<translation> The other guys are already doing it, why stop ISPs? </translation>

ISP surveillanceObservation of consumers is neither Comprehensive, nor Unique

<quote> The Working Paper addresses two fundamental points. First, ISP access to user data is not comprehensive – technological developments place substantial limits on ISPs’ visibility. Second, ISP access to user data is not unique – other companies often have access to more information and a wider range of user information than ISPs. Policy decisions about possible privacy regulation of ISPs should be made based on an accurate understanding of these facts. </quote>

<view> It’s unargued why comprehensive or unique are bright-line standards of anything at all. </view>

Previously filled.

Mentions

Claims

  • ISPs < Media
    The dumb-pipe, bit-shoving, ISPs see less than media services, who see semantic richness.
  • Cross-device is the new nowadays.
  • Encryption is everywhere.

Definitions

Availability
  • a technical statement
  • contra “use” which is an action by a person
Cross-Device Tracking
Deterministic
Logged-In, Cross-Context Tracking
Probabilistic
Not Logged-In, Cross-Context Tracking
Cross-Device Tracking
  • Frequency Capping
  • Attribution
  • Improved Advertising Targeting
  • Sequenced Advertising
  • Tracking Simultaneity
Limits the use of “data” (facts about consumers)
  • at the point of collection
  • at the point of use
Location of a consumer
  • Coarse contra Precise
  • Current contra Historical

Summary

The document has both a Preface and an Executive Summary. so the journeyperson junior policy wonkmaker can approach the material at whatever level of complexity their time budget and training affords.

Preface

  • Technological Developments Place Substantial Limits on ISPs’ Visibility into Users’ Online Activity:
    1. From a single stationary device to multiple mobile devices and connections.
    2. Pervasive encryption.
    3. Shift in domain name lookup.
  • Non-ISPs Often Have Access to More and a Wider Range of User Information than ISPs:
    1. Non-ISP services have unique insights into user activity.
    2. Non-ISPs dominate in cross-context tracking.
    3. Non-ISPs dominate in cross-device tracking.

Executive Summary

  • Technological Developments Place Substantial Limits on ISPs’ Visibility into Users’ Online Activity:
    1. From a single stationary device to multiple mobile devices and connections.
    2. Pervasive encryption.
    3. Shift in domain name lookup.
  • Non-ISPs Often Have Access to More and a Wider Range of User Information than ISPs:
    1. Non-ISP services have unique insights into user activity.
      • social networks
      • search engines
      • webmail and messaging
      • operating systems
      • mobile apps
      • interest-based advertising
      • browsers
      • Internet video
      • e-commerce.
    2. Non-ISPs dominate in cross-context tracking.
    3. Non-ISPs dominate in cross-device tracking.

Table Of Contents

Online Privacy and ISPs: ISP Access to Consumer Data is Limited and Often Less than Access by Others

Summary of Contents:

  • Preface
  • Executive Summary
    • Appendix 1: Some Key Terms
  • Chapter 1: Limited Visibility of Internet Service Providers Into Users’ Internet Activity
    • Appendix 1: Encryption for Top 50 Web Site
    • Appendix 2: The Growing Prevalence of HTTPS as Fraction of Internet Traffic
  • Chapter 2: Social Networks
  • Chapter 3: Search Engines
  • Chapter 4: Webmail and Messaging
  • Chapter 5: How Mobile Is Transforming Operating Systems
  • Chapter 6: Interest-Based Advertising (“IBA”) and Tracking
  • Chapter 7: Browsers, Internet Video, and E-commerce
  • Chapter 8: Cross-Context Tracking
    • Appendix 1: Cross-Context Chart Citations
  • Chapter 9: Cross-Device Tracking
  • Chapter 10: Conclusion

Mentions

  • HTTPS
  • Interest-Based Advertising (IBA)
  • Tracking
  • Location
    • Coarse Location
    • Precise Location
  • Natural Language Conversation Robots (a.k.a. ‘bots)
    • Siri, Apple
    • Now, Google Now
    • Cortana, Microsoft

Argot

Also see page 124 of The Work.

  • Availability → contra Use
  • Big Data → data which is very big.
  • Broadband Internet Access Services → an ISP, but not a dialup service
    as used in the Open Internet Order, of the FCC, 2015-24, Appendix A.
  • Chat bot → <fancy>Personal Digital Assistance</fancy>
  • Cookie
  • CPNI → Customer Proprietary Network Information
    47 U.S.C. §222. Also, Section 222 are at 47 C.F.R.§ 64.2001 et seq.
  • Cross-Dontext
  • Cross-Device
  • DNS → Domain Name Service
  • DPI → Deep Packet Inspection
  • Edge Providers → smart pipes, page stuffing, click-baiting; e.g. Akamai, CloudFlare, CloudFront, etc.. exemplars.
  • End-to-End
    • Argument
    • Encryption
  • Factual Analysis → this means something different to lawyers contra engineers.
  • FCC → Federal Communications Commission
  • Form
    Form Autofill, a browser feature
  • FTC → Federal Trade Commission
  • FTT → Freedom To Tinker, a venue, an oped
  • GPS → Global Positioning System
  • HTTP → you know.
  • HTTPS → you know.
  • IBA → Interest-Based Advertising
  • IP → Internet Protocol
    • Address
  • IoT → Internet of Thingies Toys Unpatchables
  • IRL → <culture who=”The Youngs”>In Real Life</culture>
  • ISP → Internet Service Provider
  • Last Mile, of an ISP
  • Location
    • Coarse → “city”- “DMA”- or “country”-level
    • Precise → an in-industry definition exists
  • Metadata → indeed.
  • OBA → Online Behavioral Advertising
  • Open Internet Order, of the FCC.
  • OS → <ahem>Operating System</ahem>
  • Party System
    • First Party
    • [Second Party], no one cares.
    • Third Party
    • [Fourth Party]
  • Personal Information → the sacred stuff, the poisonous stuff
  • Personal Digital Assistant → a trade euphemism for NLP + command patterns for IVR; all the 1st-tier shops have one nowadays.
    • Siri → Apple
    • Now → Google
    • Cortana → Microsoft
  • Scanning
  • Section 222, see Title II
  • SSL → you mean TLS
  • Title II, of the Telecommunications Act.
    • Section 222,
  • Tracking
    • (Across-) Cross-Context
    • (Across-) Cross-Device
  • TLS → you mean SSL
  • UGC → User-Generated Content (unsupervised filth; e.g. comment spam)
  • URL → you know.
  • VPN → run one.
  • WiFi → for some cultural reason “wireless” turns into “Wireless Fidelity” and “WiFi”
  • Working Paper → are unreviewed work products..
  • Visibility → bookkeeping by the surveillor observer.

Actualities

References

Of course, it’s a legal-style policy whitepaper. Of course there are references; they are among the NN footnotes. In rough order of appearance in the work.

 

Modern MySQL++, MySQL++ v3.2.3

TangentSoft

Forks

… of unknown currency or quality.

Abandoned

Alternatives

Persuasion and the other thing: A critique of big data methodologies in politics | Ethnography Matters

Molly Sauter; Persuasion and the other thing: A critique of big data methodologies in politics; In Ethnography Matters; 2017-05-24.

tl;dr → 3026 words. Big Data (which so is very big) is bad. The sphere is problematized. A problematic which situates the hegemons is synthesized via the dialectic. A mode of resistance is posited.

<ahem>… and by way of brief rebuttal: The Computers and The Establishment that owns & operates The Computers, their work inuring to the mutual benefit of them both, individually and severally, are smarter than all that (c.f. the trivial use of “grep -v”), and also the suggested modality of dissent violates the T&C which was previously freely given and binds & constrains individual future actions; its unilateral repudiation makes the performer at once dishonest, conflicted, and an outlaw who deserves no quarter; not in theory, not in practice, or under the reigning jurisdictional supervision (c.f. 18 U.S.C. Section 1001, as opined).</ahem>

Previously filled.

Mentions

  • Cambridge Analytica
  • Donald Trump
  • Brexit Campaign
  • Facebook
    • “likes”
    • targeted nudges
  • Mother Jones
  • The Guardian
  • SCL Group
  • Apple (Computer) Inc.

Claims

  • There is not enough consent (from the subjects)
  • <quote>Democracy shifts from a form of governance at least theoretically concerned with public debate and persuasion to one focused on private, opaque manipulation and emotional coercion.</quote>

Resistance

The obfuscation schemes, taxonomized in Brunton & Nissenbaum:

  • noisy bots
  • “like-farming,” i.e. spamming.
  • TrackMeNot
    a browser extension which generates abusive search query engines.
  • AdNauseam
    a browser extension which generates abusive click streams.
  • FaceCloak
    Something about storing data “off Facebook,” yet performing the data “on Facebook.”
  • Bayesian Flooding … sounds fancy; it means creating profile- & page- spam entries on Facebook.

Who

Unless otherwise noted persons are credited as “an activist.”

  • Finn Brunton
    with Helen Nissenbaum
  • Michal Kosinski, a bad guy in the pantheon
    with et al. as David Stillwell, Thore Graepel
  • Helen Nissenbaum
    with Finn Brunton (for symmetry)
  • Kelly Oliver
  • Molly Sauter
  • Zeynep Tufekci
  • Sara Marie Watson

Argot

… sounds fancy, and more than a little dangerous (<quote> cacklingly evil</quote>).  In rough order of appearance.

  • psychographics
  • algorithmic nudging
  • entitlements (<quote> held by advertisers, tech firms, and researchers who deploy big data analytics in support of political campaigns or other political projects </quote>
  • sense of entitlement
  • subjectivity (something about having agency; being such is good)
    objects (data objects); something about not having agency; being such is bad.
  • obfuscation
  • sabotage
    <quote>sabotaging the efficacy of the methodology in general, to resist attempts to be read, known, and manipulated.</quote>
  • emotional contagion; c.f. Facebook, an ”experiment,” 2014
  • nudge (contra shove)
  • algorithmic modeling → “opinions embedded in mathematics” [page 21, O'Neil].
  • otherness
  • knowability
  • digital shadow-selves
  • a paradoxical problem
    wow man, dig it … a paradox, a problem with a paradox, that’s like a paradox2.
  • data broker
  • entitlement of inference
    <quote>a certain entitlement of inference</quote>
    <quote>the entitlement of inference on display</quote>
  • influence techniques
    secret or opaque influence techniques
  • consent of the governed
    meaningful consent
  • inferential modeling collectssynthesizes non-disclosed information
  • opting out
    social media abstinence
  • data doppelganger
  • pervasive surveillance and modeling systems
  • obfuscation
    <quote>creates noise, either at the level of the platform or the individual profile</quote>

Referenced

Trajectory Recovery from Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data | Xu, Tu, Li, Zhang, Fu, Jin

Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, Depeng Jin; Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data; In Proceedings of the Conference on the World Wide Web (WWW); 2017-02-21 (2017-02-25); 10 pages; arXiv:1702.06270

tl;dr → probabilistic individuation from timestamped aggregated population location records.

Abstract

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual’s mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users’ privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals’ trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual’s trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users’ trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.

Promotions

References

  1. R. Wang, M. Xue, K. Liu, et al. Data-driven privacy analytics: A wechat case study in location-based social networks. In Wireless Algorithms, Systems, and Applications. Springer, 2015.
  2. Apple’s commitment to your privacy.
  3. V. D. Blondel, M. Esch, C. Chan, et al. Data for development: the D4D challenge on mobile phone data. arXiv:1210.0137, 2012.
  4. G. Acs and C. Castelluccia. A case study: privacy preserving release of spatio-temporal density in Paris. In Proceedings of the ACM Conference of the Special Interest Group on Knowledge D-something and D-Something (SIGKDD). ACM, 2014.
  5. China telcom’s big data products.
  6. C. Song, Z. Qu, N. Blumm. Limits of predictability in human mobility. In Science, 2010.
  7. S. Isaacman, R. Becker, R. Cáceres, et al. Ranges of human mobility in Los Angeles and New York. In Proceedings of the IEEE Workshops on Pervasive Computing and Communications (PERCOM). IEEE, 2011.
  8. S. Isaacman, R. Becker, R. Cáceres, et al. Human mobility modeling at metropolitan scales. In In Proceedings of the ACM Conference on Mobile Systems (MOBISYS). ACM, 2012.
  9. M. Seshadri, S. Machiraju, A. Sridharan, et al. Mobile call graphs: beyond power-law and lognormal distributions. In Proceedings of the ACM Conference on Knowledge Discovery? and Discernment? (KDD). ACM, 2008.
  10. Y. Wang, H. Zang, M. Faloutsos. Inferring cellular user demographic information using homophily on call graphs. In Proceedings of the IEEE Workshop on Computer Communications (INFOCOM) IEEE, 2013.
  11. A. Wesolowski, N. Eagle, A. J. Tatem, et al. Quantifying the impact of human mobility on malaria. In Science, 2012.
  12. M. Saravanan, P. Karthikeyan, A. Aarthi. Exploring community structure to understand disease spread and control using mobile call detail records. NetMob D4D Challenge, 2013. Probably there’s a promotional micro-site for this.
  13. R. W. Douglass, D. A. Meyer, M. Ram, et al. High resolution population estimates from telecommunications data. In EPJ Data Science, 2015.
  14. H. Wang, F. Xu, Y. Li, et al. Understanding mobile traffic patterns of large scale cellular towers in urban environment. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2015.
  15. L. Sweeney. k-anonymity: A model for protecting privacy. In International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002.
  16. Y. de Montjoye, L. Radaelli, V. K. Singh, et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. In Science, 2015.
  17. H. Zang and J. Bolot. Anonymization of location data does not work: A large-scale measurement study. In Proceedings of the ACM Conference on Mobile Communications (Mobicom). ACM, 2011.
  18. M. Gramaglia and M. Fiore. Hiding mobile traffic fingerprints with glove. In Proceedings of the ACM Conference CoNEXT, 2015.
  19. A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. In Nature, 2005.
  20. A. Machanavajjhala, D. Kifer, J. Gehrke, et al. l-Diversity: Privacy beyond k-Anonymity. In Transactions on Knowledge Doodling? and Deliverance? (TKDD), 2007.
  21. Y. de Montjoye, C. A. Hidalgo, M. Verleysen, et al. Unique in the crowd: The privacy bounds of human mobility. In Scientific Reports, 2013.
  22. G. B. Dantzig. Linear Programming and Extensions. Princeton University Press, 1998.
  23. H. W. Kuhn. The Hungarian Method for the Assignment Problem. In Naval Research Logistics Quarterly, 1955.
  24. O. Abul, F. Bonchi, M. Nanni. Anonymization of moving objects databases by clustering and perturbation. In Information Systems, 2010.
  25. Pascal Welke, Ionut Andone, Konrad Blaszkiewicz, Alexander Markowetz. Differentiating smartphone users by app usage. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 519–523. ACM, 2016.
  26. Lukasz Olejnik, Claude Castelluccia, Artur Janc. Why Johnny Can’t Browse in Peace: On the uniqueness of web browsing history patterns. In Proceedings of the 5th Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs), 2012.
  27. M. C. Gonzalez, C. A. Hidalgo, A.-L. Barabasi. Understanding individual human mobility patterns. In Nature, 2008.
  28. C. Song, T. Koren, P. Wang, et al. Modelling the scaling properties of human mobility. In Nature Physics, 2010.
  29. Y. Liu, K. P. Gummadi, B. Krishnamurthy, et al. Analyzing Facebook Privacy Settings: User Expectations vs. Reality. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2011.
  30. B. Krishnamurthy and C. E. Wills. Generating a privacy footprint on the Internet. In Proceedings of the ACM Internet Measurement Conference
  31. S. Le B., C. Zhang, A. Legout, et al. I know where you are and what you are sharing: exploiting P2P communications to invade users’ privacy. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2011.
  32. S. Liu, I. Foster, S. Savage, et al. Who is. com? learning to parse WHOIS records. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2015.
  33. H. Kido, Y. Yanagisawa, T. Satoh. Protection of location privacy using dummies for location-based services. In Proceedings of the IEEE International Conference on (Mountain?) DEW (ICDEW). IEEE, 2005.
  34. A. Monreale, G. L. Andrienko, N. V. Andrienko, et al. Movement data anonymity through generalization. In Transactions on Data Privacy, 2010.
  35. K. Sui, Y. Zhao, D. Liu, et al. Your trajectory privacy can be breached even if you walk in groups. In Proceedings of the IEEE/ACM International Workshop on Quality of Service (IWQoS), 2016.
  36. Y. Song, D. Dahlmeier, S. Bressan. Not so unique in the crowd: a simple and effective algorithm for anonymizing location data. In PIR@ SIGIR, 2014.
  37. S. Garfinkel. Privacy protection and RFID. In Ubiquitous and Pervasive Commerce. Springer, 2006.
  38. J. Domingo-Ferrer and R. Trujillo-Rasua. Microaggregation-and permutation-based anonymization of movement data. In Information Sciences, 2012.
  39. Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, Salil Vadhan. Robust Traceability From Trace Amounts. In Proceedings of the 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS), , pages 650–669. IEEE, 2015.

Previously filled.