Antonio A. Casilli - Networks, complexity, and privacy

1,346 views

Published on

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

Published in: Education, Technology, Business
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,346
On SlideShare
0
From Embeds
0
Number of Embeds
555
Actions
Shares
0
Downloads
9
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

Antonio A. Casilli - Networks, complexity, and privacy

  1. 1. Networks, complexity, and privacy Antonio A. Casilli (Telecom ParisTech SES) Institut Mines-Télécom
  2. 2. Social networks 2 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  3. 3. If I say «social network» 3 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  4. 4. If I say «social network» 4 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  5. 5. If I say «social network» 5 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  6. 6. If I say «social network» 6 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  7. 7. If I say «social network» 7 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  8. 8. If I say «social network» 8 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  9. 9. If I say «social network» 9 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  10. 10. If I say «social network» 10 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  11. 11. If I say «social network» 11 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  12. 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) 12 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  13. 13. Human groups as networks 13 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  14. 14. Human groups as networks 14 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  15. 15. Human groups as networks 15 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  16. 16. Human groups as networks 16 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  17. 17. Human groups as networks Bridges Peripherals Group Members Central Members Isolate 17 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  18. 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? 18 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  19. 19. Computer-mediated interactions 1992 Robin Dunbar 148 19 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  20. 20. Computer-mediated interactions 2000 Peter Killworth 290 20 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  21. 21. Computer-mediated interactions 2010 Matthew Salganik 610 21 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  22. 22. 2012 22 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  23. 23. Computer-mediated interactions 1969: six degrees of separation 23 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  24. 24. Computer-mediated interactions 2012: four degrees of separation 24 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  25. 25. Computer-mediated interactions  Two possible explanations  Higher transitivity of online networks  Presence of big hubs 25 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  26. 26. Computer-mediated interactions Different types of online «social capital» Bonding  Bonding : homogenous groups and cohesion  Bridging : information circulating among heterogenous groups Bridging 26 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  27. 27. Computer-mediated interactions From a “little boxes” society 27 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  28. 28. Computer-mediated interactions …to “networked individualism”? 28 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  29. 29. Computer-mediated interactions 29 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  30. 30. Computer-mediated interactions “Glocal” networks 30 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  31. 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 31 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  32. 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’ ». 32 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  33. 33. A social media experiment 1. Two Facebook profiles initial state 33 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  34. 34. A social media experiment 2. Profile 1 discloses personal preferences 34 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  35. 35. A social media experiment 3. Profile 1 discloses bio 35 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  36. 36. A social media experiment 4. Profile 1 uploads a photo album 36 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  37. 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) 37 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  38. 38. A social media experiment Bonding Bridging 38 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  39. 39. A social media experiment 39 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  40. 40. Studying complexity 40 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  41. 41. Complexity and social science  Chaos, social dynamics, emergent behaviours 41 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  42. 42. Complexity and social science  Social systems, self-organization, autopoiesis, complex adaptive systems 42 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  43. 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. 43 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  44. 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. 44 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  45. 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… 45 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  46. 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. 46 11/19/2013 Institut Mines-Télécom . 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. 47. Agent-based modelling 47 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  48. 48. Agent-based modelling 48 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  49. 49. Agent-based modelling 49 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  50. 50. Privacy 50 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  51. 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)? 51 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  52. 52. The end of privacy online? 52 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  53. 53. The end of privacy online? 53 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  54. 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 54 Facebook announces acquisition of facial recognition technology company Face.com Télécom 11/19/2013 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. 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 55 11/19/2013 Institut Mines-Télécom 1 2 Télécom ParisTech
  56. 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? 56 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  57. 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. 57 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  58. 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. 58 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  59. 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? 59 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  60. 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: • • 60 11/19/2013 Mean privacy level; Number and size of components. Institut Mines-Télécom Télécom ParisTech
  61. 61. Our simulation model  Resulting system configurations (1) Echo-chambers 61 11/19/2013 (2) Large components Institut Mines-Télécom (3) Generalized connectedness. Télécom ParisTech
  62. 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. 62 11/19/2013 Institut Mines-Télécom Télécom ParisTech
  63. 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 • • 63 11/19/2013 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. Télécom ParisTech
  64. 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. 64 11/19/2013 Institut Mines-Télécom Evolution of mean privacy over time, with high bridging social capital and high cultural openness. Télécom ParisTech
  65. 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. 65 11/19/2013 Institut Mines-Télécom Privacy cycles in the presence of service provider interventions to unlock privacy setting by default Télécom ParisTech
  66. 66.  Thank you!  Email : antonio.casilli@telecom-paristech.fr  Blog : http://www.bodyspacesociety.eu  Twitter : @bodyspacesoc 66 11/19/2013 Institut Mines-Télécom Télécom ParisTech

×