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Auto-biography, Mobile Social
Life-Logging, and the transition
   from ephemeral to archival
            society

                  Marc A. Smith
               Chief Social Scientist
                     Telligent
             Marc.Smith@telligent.com
    Studying Society in a Digital World – Princeton University
                         April 24th, 2009
Patterns are left behind
                     2
Many organizations are
adopting social media


• Use of these tools creates data sets that map their
  internal social network structure as an accidental by-
  product.
• Studying these data is sets is a focus of growing
  interest.
• Research projects like SenseCam are now becoming
  products and services like nTag, Spotme, Fire Eagle,
  and Google Latitude while devices like iPhone and
  G1 are weaving location into every application.
Information wants to be copied
Bits exist along a gradient
        from private to public.

But in practice they only move in one direction.
Strong links
 between
people and
 content…
…are as str
as the weakest
Cryptography weakens over time
      •     Eventually, private
             bits, even when
           encrypted, become
           public because the
          march of computing
           power makes their
                encryption
          increasingly trivial to
                   break.
No one expects
privacy to be
perfect in the
physical world.
Unintended cascades
Taking a photo or updating a status message can
now set off a series of unpredictable events.
Additional sensors will collect medical data to
improve our health and safety, as early adopters
in the quot;Quantified Selfquot; movement make clear.
Continuous data collection

               Microsoft Research,
               Cambridge, UK:
               “SenseCam”
When my phone notices your phone

              a new set of
  mobile social software applications
         become possible that
   capture data about other people
            as they beacon
    their identifies to one another.
Interactionist
  Sociology

• Central tenet
   – Focus on the active effort of
     accomplishing interaction
• Phenomena of interest
   –   Presentation of self
   –   Claims to membership
   –   Juggling multiple (conflicting) roles
   –   Frontstage/Backstage
   –   Strategic interaction
   –   Managing one’s own and others’ “face”
• Methods
   – Ethnography and participant observation

   (Goffman, 1959; Hall, 1990)
Innovations in the interaction order:

45,000 years ago: Speech, body adornment
10,000 years ago: Amphitheater
 5,000 years ago: Maps
   150 years ago: Clock time

    -2 years from now: machines with
                       social awareness
17
Sensors, Routes, Community
     Community Aspects: A Sociological Revolution?

   SpotMe: Wireless device for meetings and events
19
Trace Encounters: http://www.traceencounters.org/
21
Social Network
 Theory
• Central tenet
    – Social structure emerges from
      the aggregate of relationships (ties)
      among members of a population
• Phenomena of interest
    – Emergence of cliques and clusters
      from patterns of relationships
    – Centrality (core), periphery (isolates),      Source: Richards,
      betweenness                                   W. (1986). The
                                                    NEGOPY network
• Methods                                           analysis program.
                                                    Burnaby, BC:
    – Surveys, interviews, observations, log file   Department of
      analysis, computational analysis of           Communication,
      matrices                                      Simon Fraser
                                                    University. pp.7-16
  (Hampton &Wellman, 1999; Paolillo,
  2001; Wellman, 2001)
24
Patterns of connection
               may uniquely identify

De-anonymizing Social Networks
Arvind Narayanan & Vitaly Shmatikov
http://33bits.org/2009/03/19/de-anonymizing-social-networks/

Abstract:
Operators of online social networks are increasingly sharing potentially sensitive information
about users and their relationships with advertisers, application developers, and data-mining
researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a
new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its
effectiveness on real-world networks, we show that a third of the users who can be verified to
have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-
sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our
de-anonymization algorithm is based purely on the network topology, does not require creation
of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works
even when the overlap between the target network and the adversary’s auxiliary information is
small.
Distinguishing attributes:
        • Answer person
          – Outward ties to local isolates
          – Relative absence of triangles
          – Few intense ties


        • Reply Magnet
          – Ties from local isolates often
            inward only
          – Sparse, few triangles
          – Few intense ties


                                             26
Distinguishing attributes:
        • Answer person
          – Outward ties to local isolates
          – Relative absence of triangles
          – Few intense ties


        • Discussion person
          – Ties from local isolates often
            inward only
          – Dense, many triangles
          – Numerous intense ties


                                             27
http://w


NodeXL

Network
Overview
Discovery
Exploration
Tag Ecologies I




       Adamic et al. WWW 2008
Result: lives that are more publicly
    displayed than ever before.

 Add potential improvements in audio and facial
   recognition and a new world of continuous
      observation and publication emerges.

Some benefits, like those displayed by the Google
  Flu tracking system, illustrate the potential for
       insight from aggregated sensor data.

  More exploitative applications are also likely.
Auto-biography, Mobile Social
Life-Logging, and the transition
   from ephemeral to archival
            society

                  Marc A. Smith
               Chief Social Scientist
                     Telligent
             Marc.Smith@telligent.com
    Studying Society in a Digital World – Princeton University
                         April 24th, 2009

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Autobiography, Mobile Social Life-Logging and the Transition from Ephemeral to Archival Society

  • 1. Auto-biography, Mobile Social Life-Logging, and the transition from ephemeral to archival society Marc A. Smith Chief Social Scientist Telligent Marc.Smith@telligent.com Studying Society in a Digital World – Princeton University April 24th, 2009
  • 2. Patterns are left behind 2
  • 3. Many organizations are adopting social media • Use of these tools creates data sets that map their internal social network structure as an accidental by- product. • Studying these data is sets is a focus of growing interest. • Research projects like SenseCam are now becoming products and services like nTag, Spotme, Fire Eagle, and Google Latitude while devices like iPhone and G1 are weaving location into every application.
  • 5. Bits exist along a gradient from private to public. But in practice they only move in one direction.
  • 7. …are as str as the weakest
  • 8. Cryptography weakens over time • Eventually, private bits, even when encrypted, become public because the march of computing power makes their encryption increasingly trivial to break.
  • 9. No one expects privacy to be perfect in the physical world.
  • 10. Unintended cascades Taking a photo or updating a status message can now set off a series of unpredictable events.
  • 11. Additional sensors will collect medical data to improve our health and safety, as early adopters in the quot;Quantified Selfquot; movement make clear.
  • 12.
  • 13. Continuous data collection Microsoft Research, Cambridge, UK: “SenseCam”
  • 14. When my phone notices your phone a new set of mobile social software applications become possible that capture data about other people as they beacon their identifies to one another.
  • 15. Interactionist Sociology • Central tenet – Focus on the active effort of accomplishing interaction • Phenomena of interest – Presentation of self – Claims to membership – Juggling multiple (conflicting) roles – Frontstage/Backstage – Strategic interaction – Managing one’s own and others’ “face” • Methods – Ethnography and participant observation (Goffman, 1959; Hall, 1990)
  • 16. Innovations in the interaction order: 45,000 years ago: Speech, body adornment 10,000 years ago: Amphitheater 5,000 years ago: Maps 150 years ago: Clock time -2 years from now: machines with social awareness
  • 17. 17
  • 18. Sensors, Routes, Community Community Aspects: A Sociological Revolution? SpotMe: Wireless device for meetings and events
  • 19. 19
  • 21. 21
  • 22.
  • 23. Social Network Theory • Central tenet – Social structure emerges from the aggregate of relationships (ties) among members of a population • Phenomena of interest – Emergence of cliques and clusters from patterns of relationships – Centrality (core), periphery (isolates), Source: Richards, betweenness W. (1986). The NEGOPY network • Methods analysis program. Burnaby, BC: – Surveys, interviews, observations, log file Department of analysis, computational analysis of Communication, matrices Simon Fraser University. pp.7-16 (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
  • 24. 24
  • 25. Patterns of connection may uniquely identify De-anonymizing Social Networks Arvind Narayanan & Vitaly Shmatikov http://33bits.org/2009/03/19/de-anonymizing-social-networks/ Abstract: Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc. We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo- sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.
  • 26. Distinguishing attributes: • Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties • Reply Magnet – Ties from local isolates often inward only – Sparse, few triangles – Few intense ties 26
  • 27. Distinguishing attributes: • Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties • Discussion person – Ties from local isolates often inward only – Dense, many triangles – Numerous intense ties 27
  • 29.
  • 30. Tag Ecologies I Adamic et al. WWW 2008
  • 31.
  • 32. Result: lives that are more publicly displayed than ever before. Add potential improvements in audio and facial recognition and a new world of continuous observation and publication emerges. Some benefits, like those displayed by the Google Flu tracking system, illustrate the potential for insight from aggregated sensor data. More exploitative applications are also likely.
  • 33. Auto-biography, Mobile Social Life-Logging, and the transition from ephemeral to archival society Marc A. Smith Chief Social Scientist Telligent Marc.Smith@telligent.com Studying Society in a Digital World – Princeton University April 24th, 2009