ATHENS programme seminar by Antonio A. Casilli (Telecom ParisTech, Nov. 19, 2013).

1. Networks, complexity,
and privacy
Antonio A. Casilli
(Telecom ParisTech SES)
Institut Mines-Télécom

2. Social networks
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3. If I say «social network»
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4. If I say «social network»
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5. If I say «social network»
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6. If I say «social network»
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7. If I say «social network»
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8. If I say «social network»
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9. If I say «social network»
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10. If I say «social network»
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11. If I say «social network»
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12. Human groups as networks
Social network: a way of describing human groups as a set of
social actors (nodes) and relationships existing among them
(ties)
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13. Human groups as networks
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14. Human groups as networks
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15. Human groups as networks
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16. Human groups as networks
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17. Human groups as networks
Bridges
Peripherals
Group
Members
Central Members
Isolate
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18. Computer-mediated interactions
 Is computer-mediated interaction changing the overall
structure of human networks?
 Comparing computer-mediated and face-to-face
relationships: which networks are larger?
 Further refinements: are personal networks mainly
composed of "strong" or "weak" ties? Are there more weak
ties in online personal networks?
 Are personal networks densely knitted, or sparse? Are online
personal networks sparser?
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19. Computer-mediated interactions
1992 Robin Dunbar
148
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20. Computer-mediated interactions
2000 Peter Killworth
290
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21. Computer-mediated interactions
2010 Matthew Salganik
610
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22. 2012
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23. Computer-mediated interactions
1969: six degrees of separation
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24. Computer-mediated interactions
2012: four degrees of separation
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25. Computer-mediated interactions
 Two possible
explanations
 Higher transitivity of online
networks
 Presence of big hubs
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26. Computer-mediated interactions
Different types of online «social
capital»
Bonding
 Bonding : homogenous groups
and cohesion
 Bridging : information
circulating among heterogenous
groups
Bridging
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27. Computer-mediated interactions
From a “little boxes” society
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28. Computer-mediated interactions
…to “networked individualism”?
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29. Computer-mediated interactions
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30. Computer-mediated interactions
“Glocal” networks
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31. A social media experiment
 Experiment: create two accounts
 The fomer (actual profile) discloses
more personal details, the latter
(control profile) discloses less
 Invite 100 users to friend them (50
each)
 Friends provide feedback on how to
enrich profiles (Comments,
Messages, Likes, Shares)
 Compare two accounts over 50
days
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32. A social media experiment
 Observation notes:
– « Jusqu’à aujourd’hui, les retours sur les deux
profils sont assez négatifs. Les connaissances
de sexe féminin surtout ne se gênent pas pour
exprimer leur aversion. Une amie définit le profil
1 comme ‘effrayant’, une autre qualifie la photo
du profil 2 de ‘monstrueuse’ ».
–« Indication : utilisateur du profil 1 apprécie la
cuisine japonaise et écoute de la musique punk.
Il lit des bandes dessinées et des poètes de la
beat generation ».
–« Profil 1 constamment ouvert dans mon
navigateur. En automatique des petites fenêtres
contenant des suggestions ou des ‘morceaux
choisis’ par ses amis. ‘L’utilisatrice X est fan de
l’artiste peintre Tel’ ; ‘L’utilisateur Y a aimé le
dernier livre de l’écrivain Telautre’ ».
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33. A social media experiment
1. Two Facebook profiles initial state
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34. A social media experiment
2. Profile 1 discloses personal preferences
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35. A social media experiment
3. Profile 1 discloses bio
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36. A social media experiment
4. Profile 1 uploads a photo album
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37. A social media experiment
 Compare social graphs
 Disclosing profile has a larger, more
varied network
 Better management of social capital:
balance bw bonding (social
cohesion) and bridging (social
connectivity)
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38. A social media experiment
Bonding
Bridging
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39. A social media experiment
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40. Studying complexity
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41. Complexity and social science
 Chaos, social dynamics,
emergent behaviours
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42. Complexity and social science
 Social systems, self-organization,
autopoiesis, complex adaptive systems
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43. Agent-based modelling
 Agent-based computer
simulations
 Generate socially consistent
scenarios on a computer;
 Analyse the resulting scenario
outcomes to:
 Identify sufficient conditions
under which different
outcomes emerge;
 Assess their sensitivity to
parameter changes.
 An aid to perform a thought
experiment.
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44. Agent-based modelling
 The logic of an agent-based
model
 Generate an artificial
population of agents in an
environment;
 Endow them with basic rules
of behaviour;
 Let them interact for a certain
time and step aside;
 Observe outcomes at the
system level at the end.
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45. Agent-based modelling
 KISS (Keep It Simple and
Stupid)
 Schelling‟s segregation model
(1973)
 How tolerant individuals have
to be in order to avoid
collective segregation (the
creation of ghettoes) in a
given social space?
 Some surprising results…
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46. Agent-based modelling
„„Pure‟‟ models
„„Empirical‟‟ models
. Built by abstraction from a target
. Open to estimation and validation via
system (a social phenomenon or context). qualitative and quantitative data.
. Mainly regarded as tools for
generating, expressing and testing
theories.
. Not always realistically representing
choices and behaviors at the micro level.
. Enable in-depth reflection on the
possible unintended social consequences
of purposeful individual actions.
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. Quantitative data can be used to
assess the probability that a certain event
takes place within a given population of
agents (either predictively or
retrodictively).
. Use of qualitative data to inform
simulation rules and parameters is also
attested since the late 1990s (structural
validation).
Télécom ParisTech

47. Agent-based modelling
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48. Agent-based modelling
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49. Agent-based modelling
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50. Privacy
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51. The end of privacy online?
 The privacy challenge in
social media
 Periodic privacy incidents on
FB
 Mark Zuckerberg: ”Public is
the new social norm”
 Are we approaching the “End
of Privacy” as we know it?
 Alleged tendency to
"renounce privacy" for an
open, connected existence
(publicness)?
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52. The end of privacy online?
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53. The end of privacy online?
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54. Date
Privacy-related incident
Users’ reaction
05/09/2006
Introduction of News Feed (content and user
updates aggregator).
Users’ uproar over the default opt-in policy. Creation of the advocacy group “Students
against Facebook News Feed” to protest the new feature. The group attracts almost
300,000 members, leading to apologies by Mark Zuckerberg, Facebook’s funder and
CEO.
26/09/2006
Facebook reinforces privacy options for users
(to limit searchability and tie formation) to
anticipate the gradual opening of its
membership to any US and Canada college
students with a valid email address and over
the age of 13.
06/11/2007
Introduction of Beacon (advertising system
aggregating purchase data over several
platforms, most prominently Amazon).
Prominent political activist group MoveOn.org creates an online petition against Beacon.
Their Facebook group reaches 50,000 members, which leads Mr Zuckerberg to issue an
official apology. Beacon ultimately shut down in September 2009.
09/12/2009
Facebook changes its privacy settings,
making sharing with everyone compulsory:
legal names, profile pictures, and gender are
now public by default.
An alliance of privacy organisations files a complaint with America’s Federal Trade
Commission (FTC).
21/04/2010
Facebook introduces the Like button social
plugin for external websites. Users can now
log in, like and share contents (“frictionless
sharing”) on other services through their
Facebook account.
Prompted by their constituents, a group of American senators asks the FTC to establish
privacy guidelines for Facebook. Privacy groups file a formal complaint to the FTC
against Facebook’s “unfair and deceptive trade practice of sharing user information with
the public and with third-party application developers”. At the end of May 2010, Mr
Zuckerberg announces new and simplified privacy settings.
14/01/2011
Facebook makes users’ addresses and phone
numbers available to external websites.
After negative feedback from users, Facebook disables the feature. At the end of the
month, the fan page of Mr Zuckerberg is hacked and compromised. The following day,
Facebook starts implementing https secure pages.
08/2011
Following a series of complaints filed by
Austrian student association Europe v.
Facebook. org, it emerges that Facebook fails
to comply with the rule of allowing its users to
download their own personal data: it provides
only 39 over 84 personal data categories.
Negative media attention and creation of several campaigns requiring Facebook to give
users full access to their data.
05/2012
Facebook proposes a new and more complex
privacy policy while asking for generic “users’
feedback”.
40,000 user comments force vote on proposed alternatives to privacy policies.
20/06/2012
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Facebook announces acquisition of facial
recognition technology company Face.com
Télécom
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Institut of users’ biometric
Mines-Télécom
(creates
database
ParisTech
information through photo-tagging).
Privacy advocacy groups file complaint to the FTC recommending suspension of facial
recognition technology and protesting creation of biometric profiles of users without
their explicit consent.

55. Modelling privacy
 To find an answer to this
question let‟s try and
build an agent-based
model that represent the
possible equilibriums for
a system of agents
disclosing personal
informations online
 Phase 1: empirical
observation
 Phase 2: modelling
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2
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56. Modelling privacy
 Remember our experiment
on disclosure
 Personal network of actual
profile continues to grow in
size and displays a
distinctive balance between
social cohesion (bonding)
and social connectedness
(bridging)
 Disclosure is crucial: does
this necessarily validate the
„End-of-privacy‟
hypothesis?
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57. Modelling privacy
 Problematizing privacy
 In fact, online interactions
complexify the very notion of
privacy
 Traditional notion based on
metaphor of concentric circles
of intimacy
 Mono-directional notion: a
core of sensitive data to be
protected.
 This notion no longer seems
adapted to interactions in a
networked society.
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58. Modelling privacy
 Privacy as a multi-directional,
dynamic process
 Online privacy better
described through multidirectional negotiation
 Individuals send signals to,
and receive feedback from,
their social environment.
 Self-disclosure accompanies
adaptation to signals from the
(social) environment over
time.
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59. Modelling privacy
 We need to design a social system with:
 Formation of personal networks through bonding
and bridging ;
• Disclosure needed to form ties;
• Adaptation to signals from the environment through a
feedback process;
 What will be the final configuration of the system, in
terms of degree of disclosure?
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60. Our simulation model
 Behavioral rules:
•
•
•
Tie formation allowing for both
bonding and bridging social
capital;
Binary on/off visibility settings;
Homophilous choice of network
contacts.
 Parameters:
•
•
Tendency to value bonding /
bridging social capital;
Openness to cultural diversity.
 Indicators:
•
•
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Mean privacy level;
Number and size of components.
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61. Our simulation model
 Resulting system configurations
(1) Echo-chambers
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(2) Large components
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(3) Generalized connectedness.
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62. Our simulation model
 How parameter values affect results
Treemap: varying modes of valuing bonding/bridging ties and levels of cultural openness. Size of
rectangles is proportional to size of largest network component, colour represents differences in number of
components.
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63. Our simulation model
 Effects on social division
•
•
When bonding prevails, echochambers always emerge
regardless of the cultural
openness of agents;
When bridging prevails, the
degree of cultural openness
determines whether the result is
one or few large components.
 Effects on privacy choices
•
•
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When bonding prevails, average
privacy changes little regardless
of the cultural openness of
agents;
When bridging prevails, high
cultural openness prompts
increased privacy protection.
Institut Mines-Télécom
Evolution of mean privacy over time, with high
bridging social capital and high cultural openness.
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64. Results
 Network structure matters
• Relative value of
bonding/bridging ties affects
final outcomes;
• Homophily need not be socially
divisive;
 Important to focus on
motivations on people to
form social capital online;
 Networking service
architecture likely to play a
key role.
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Evolution of mean privacy over time, with high
bridging social capital and high cultural openness.
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65. Results
 No “End of Privacy” in
sight
 Social media usage is
not bound to destroy
privacy
 It is when
connectedness is at its
highest that privacy resurfaces;
 It becomes important to
consider users‟ attitudes
in discussions of
providers‟ privacy
policies.
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Privacy cycles in the presence of service provider
interventions to unlock privacy setting by default
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66.  Thank you!
 Email : antonio.casilli@telecom-paristech.fr
 Blog : http://www.bodyspacesociety.eu
 Twitter : @bodyspacesoc
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