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.

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

8. advertiser
shop
analytics
tracker
social
network
search engine
shop
employer
tracker
social network
bookmark
bank

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!

14. HTTP
Ontology
Online
Activities
Ontology
Parameters
and Website
info.
Web Site
Information
Location
Information
Personal
Information
Trust Model

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

23. http://youtu.be/iFocaRtLdQg

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.

25. semprivacy.com / @mdaquin