Semantic Web Technologies for Social Translucence and Privacy Mirrors on the Web
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This document discusses how semantic web technologies can help with social translucence and privacy. It proposes building "web privacy mirrors" that use semantic technologies to collect and integrate data about a user's online activities and the external entities interacting with them. This integrated data would be represented semantically so it can be understood and acted on by the user to better manage their privacy and control over personal information. Several challenges of such an approach are outlined. In the end, the document calls for the development of semantic web privacy mirrors to help users better interpret their online activities and privacy consequences.
Semantic Web Technologies for Social Translucence and Privacy Mirrors on the Web
- 1. Semantic Web Technologies for Social Translucence and Privacy Mirrors on the Web Mathieu d’Aquin and Keerthi Thomas Knowledge Media Institute, The Open University @mdaquin / semprivacy.com
- 2. Privacy? Privacy is not simply an absence of information about us in the minds of others, rather it is the control we have over information about ourselves -- Charles Fried - 1968
- 3. Privacy? Privacy is not simply an absence of information about us in the minds of others, rather it is the control we have over information about ourselves -- Charles Fried - 1968
- 4. Privacy? user input System output Privacy is not simply an absence of information about us in the minds of others, rather it is the control we have over information about ourselves -- Charles Fried - 1968
- 5. Privacy? user System output input Privacy is not simply an absence of information about us in the minds of others, rather it is the control we have over feedback information about ourselves -- Charles Fried - 1968
- 6. Privacy? user Web privacy state activities Privacy is not simply an absence of information about us in the minds of others, rather it is the control we have over feedback information about ourselves -- Charles Fried - 1968
- 7. Or in other words, Social Translucence In systems with a social process component, to achieve coherent behaviours, it is necessary for the system to make such behaviours visible and understandable to the users. -- Erickson and Kellogg, 2000
- 9. advertiser shop analytics tracker social network search engine shop employer tracker social network Web privacy (translucent) mirror bookmark bank
- 10. Examples (from mostly non-semantic worlds) • Track the tracker – Ghostery – Collusion – Spy Watch • Personal analytics – Wolfram|Alpha – Moluti – Attention Recorder
- 11. Challenges • Collecting and managing data about the user activities and the extermal world affecting them • Integrate this information in a coherent, explorable base • Make sense of this information so that it can be understood (and so acted upon by the user) ?
- 12. Challenges • Collecting and managing data about the user activities and the extermal world affecting them • Integrate this information in a coherent, explorable base • Make sense of this information so that it can be understood (and so acted upon by the user) That reminds me of something…
- 13. Challenges • Collecting and managing data about the user activities and the extermal world affecting them • Integrate this information in a coherent, explorable base • Make sense of this information so that it can be understood (and so acted upon by the user) Ah, I know: Semantic Web Technologies!
- 15. Personal Analytics d'Aquin, Elahi, Motta. Semantic technologies to support the user-centric analysis of activity data. SDoW 2011 at ISWC 2011 Thomas and d'Aquin. On the privacy implications of releasing consumer activity data. KMi Tech. Report kmi-13-02, 2013.
- 16. time agents interests d'Aquin, Elahi, Motta. Personal monitoring of web information exchange: Towards web lifelogging. WebSci10.
- 17. Trust in websites Sensitivity of data d'Aquin, Elahi, Motta. Semantic monitoring of personal web activity to support the management of trust and privacy. SPOT 2010 at ESWC 2010
- 18. Dealing with complex and sophisticated privacy situations…
- 19. Facebook graph API Facebook Ontology Basic linked data Epistemic logic theory of Facebook Ontological inference (types, relations) Epistemic inference (who knows what)
- 20. Facebook Ontology (extract) Place Agent author subclass likes Person in Post includes subclass Video App author Photo on Comment scope Status update {Everyone, Friends_of_Friends, All_Friends, Custom}
- 21. Example epistemic rules Ka Post(X) :- author(X, a) Ka Post(X) :- scope(X, All_Friends), author(X, Y), friend(Y, a) Ka Post(X) :- includes(X,Y), friend(Y, a) Ka wasIn(P, Y) :- includes(X,Y), in(X,P), Ka Post(X) Ka wasWith (Y,Z) :- includes(X, Y), include(X,Z), Ka Post(X)
- 22. d'Aquin, Thomas. Modeling and reasoning upon facebook privacy settings. Demo at ISWC 2013
- 24. Message and call for action Semantic Web Technologies can help individuals in better interpreting their own activities in terms of privacy consequences. Build semantic, web privacy mirrors! Some simple examples. Quite many more challenges.