=Paper=
{{Paper
|id=None
|storemode=property
|title=Modeling and Reasoning Upon Facebook Privacy Settings
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_36.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/dAquinT13
}}
==Modeling and Reasoning Upon Facebook Privacy Settings==
https://ceur-ws.org/Vol-1035/iswc2013_demo_36.pdf
Modeling and Reasoning Upon Facebook
Privacy Settings
Mathieu d’Aquin and Keerthi Thomas
Knowledge Media Institute, The Open University, Milton Keynes, UK
{mathieu.daquin, keerthi.thomas}@open.ac.uk
Abstract. Understanding the way information is propagated and made
visible on Facebook is a difficult task. The privacy settings and the rules
that apply to individual items are reasonably straightforward. However,
for the user to track all of the information that needs to be integrated
and the inferences that can be made on their posts is complex, to the
extent that it is almost impossible for any individual to achieve. In this
demonstration, we investigate the use of knowledge modeling and rea-
soning techniques (including basic ontological representation, rules and
epistemic logics) to make these inferences explicit to the user.
1 Introduction
The notion of social translucence (as defined in [4]) concerns the design of sys-
tems with a social process component, to achieve coherent behaviours from the
user(s) through making such behaviours visible and understandable to them.
This notion is especially relevant in relation to privacy, where the principles
of visibility, awareness and accountability promoted by social translucence are
used to enable a coherent and informed behaviour from the users with respect
to the distribution and propagation of their personal information. This idea is
well illustrated in the notion of “Privacy Mirrors”, i.e., systems that integrate the
necessary tools of “awareness and control to understand and shape the behaviour
of the system” [6].
While these notions might appear to naturally apply to social networking
systems such as Facebook1 , their privacy settings and of the mechanisms for in-
formation sharing they implement are only deceptively simple: for an individual
user to keep track of all the necessary elements to understand what information
others might have access to, and what inferences they might derive from it, is
actually too complex to be achieved. For example, while individual photos have
specific privacy scopes, the tagging, comments, likes, geographical information
attached to them can make much more information about the user available
to much more people than the user might intend, without the user’s ability to
understand the full scope of the implications of such sharing and tagging.
In this demonstration, we show how a privacy mirror for Facebook can be
implemented using knowledge modeling and reasoning techniques, to make ex-
plicit to the user some of the inferences that can be made out of information
available about them on the social platform. We use basic ontology modeling,
rules and a simplification of the basic concepts of epistemic logics.
1
http://facebook.com
2 Information from Facebook
In this demonstration, to simplify the discussion, we focus on information about
photos, especially the ones (explicitly) referring to the user. However, the basic
notions and approach described apply similarly to other types of information.
The basic concepts extracted using the Facebook Graph API2 concern people
(users), photos, comments, places and dates. Individuals (variables and con-
stants) therefore represent instances of these concepts. Predicates represent rela-
tionships. For example, users can be friends with each-other (f riend(bob, alice)),
a photo can be at a place (photoAt(photo1, segovia)), at a certain date
(date(photo1, 08 − 07 − 2013)) and include some users (onP hoto(photo1, bob)).
Finally, any post including photos have a privacy scope which could be everyone,
f riendof f riend, allf riends, custom (e.g., scope(photo1, allf riends)).
3 Basic ontological modeling and reasoning
From the explicit data extracted from Facebook, basic information can be in-
ferred using ontology-based mechanisms. For example, including range and do-
main information associated with the predicates mentioned above can help iden-
tifying types of objects (e.g., f riend(bob, alice) implies that person(bob) and
person(alice)). Similarly, using constructs available in OWL 2, the f riend pred-
icate can be declared to be reciprocal (as it is in Facebook): f riend(bob, alice)
implies f riend(alice, bob).
Property hierarchies can also be used to introduce intermediary predicates,
more abstract than the notions explicitly available in Facebook. For example,
declaring f riend as a sub-property of know (so that f riend(bob, alice) implies
know(bob, alice)). The same mechanisms, combined with the property composi-
tion construct available in OWL 2, can be used to represent much more com-
plex inferences (e.g., that if two people are on the same photo, they know each
other). However, for convenience, we choose to use rules (which can also be used
for other types of inferences not feasible with basic OWL constructs) for such
complex implications.
4 Rule-based inference
As mentioned above, some more complex inferences need to be represented that
are not conveniently achieved with ontological constructs. This includes informa-
tion such that being on a picture, geotagged with a certain place, implies that the
user was at that place (wasIn(P er, P l) :- onP hoto(P ic, P er), photoAt(P ic, P l))
or that two users on the same photo know each other (know(P er1, P er2)
:- onP hoto(P ic, P er1), onP hoto(P ic, P er2)), and possibly that they were at the
same place.
2
https://developers.facebook.com/docs/reference/api/
5 Epistemic inference
The mechanisms described above make it possible for the model to explicitly
make the inferences possible from the information being shared. However, the
important aspect here is not only which inferences can be made, but who can
make them. To address this, we use notions from epistemic logics [5]. Indeed,
epistemic logics are a type of logic that allows one to express not only statements
about the world, but also about the way the world is perceived or known by
agents in the world. In such a logic, a statement of the form Ka α indicates that
the agent a ‘knows’ the statement α to be true. Basic properties, such as the one
of self reflection (i.e., Ka α → Ka Ka α) and rules can be used to reason upon
the knowledge agents have of some information.
This framework, combined with information about the privacy settings of
Facebook, allows us to express information regarding which user might have ac-
cess to what item of information. Straightforwardly for example, information on
who knows about a photo can be derived from the privacy scope of the photo
(e.g., Ka photo(P ic) :- author(P ic, P er), scope(P ic, allf riends), f riend(P er, a)).
More complex mechanisms are also represented using this type of rules however,
for example that the friends of somebody tagged in a photo would know about
the photo, or that knowing about a photo implies knowing all the information
attached to a photo and the possible inferences that can be made from them
(e.g., that somebody was in a certain place with somebody else).
6 Implementation
Fig. 1. Screenshots of the system making explicit privacy inferences in Facebook.
The implementation of the system showing to a user the inferences that can
be made from information sharing items concerning them (currently focusing on
photos) and by who is a Web-based interface built in PHP and Javascript, that
allows the user to connect to their Facebook account and extract the relevant
information. The knowledge representation and reasoning mechanisms described
above is delegated to a Prolog-based API, carrying out the ontological reasoning
(through a basic mapping between OWL and Prolog), the rule-based reasoning
and a simplified implementation of epistemic rules described in the previous
sections.
As shown in the screenshots of Figure 1, the system displays the inferred
information to the user: 1- the people they are friend with, the ones they know
(without being friends) and the people the user might not know, but who might
have access to some of their information; 2- the photos depicting the user; 3- the
places where the user have been (who with and on what date). Clicking on a
person (as shown on Figure 1) displays information about items shared by this
user, as well as the information they know about the logged-in user. Clicking on
an item displays the information that can be inferred from this item, and the
people who might have access to these inferences.
7 Conclusion
Our goal in this demonstration is to show that knowledge modeling and reason-
ing techniques can support the notion of privacy mirrors, in systems where the
privacy implications of information sharing are complex and difficult for a user to
keep track of. In the demonstration, participants will be able to connect the sys-
tem to their own Facebook account, to check whether the results are surprising,
concerning or on the contrary, just reassuring (which are the types of reactions
we uncovered in another study [3]). In terms of future work, besides completing
and validating the modeling of Facebook’s privacy mechanisms (which can be
a complex task), one of the interesting research directions is to integrate the
model of Facebook with other sources of personal information sharing (using for
example techniques described in some of our previous works, e.g., [1, 2]). The
other direction we plan to investigate is the use of more sophisticated knowledge
representation techniques to deal with the complexity of online social situations,
including uncertainty and different levels of epistemic knowledge of information
(e.g., having access to information vs. having surely seen a piece of information).
References
1. M. dAquin, S. Elahi, and E. Motta. Personal monitoring of web information ex-
change: Towards web lifelogging. Web Science, 2010.
2. M. d’Aquin, S. Elahi, and E. Motta. Semantic technologies to support the user-
centric analysis of activity data. In Social Data on the Web (SDoW) workshop at
ISWC, 2011.
3. M. d’Aquin and K. Thomas. Consumer activity data: Usages and challenges. Knowl-
edge Media Institute, Tech. Report kmi-12-03, 2012.
4. T. Erickson and W. A. Kellogg. Social translucence: an approach to designing
systems that support social processes. ACM transactions on computer-human in-
teraction (TOCHI), 7(1):59–83, 2000.
5. J.-J. Ch. Meyer and W. van der Hoek. Epistemic Logic for AI and Computer
Science. Cambridge University Press, 2004.
6. E. D. Nguyen, D. H. ; Mynatt. Understanding and shaping socio-technical ubiqui-
tous computing systems. GVU Technical Report;GIT-GVU-02-16, 2002.