=Paper=
{{Paper
|id=Vol-2482/paper20
|storemode=property
|title=Enhancing Privacy Awareness in Online Social Networks: a Knowledge-Driven Approach
|pdfUrl=https://ceur-ws.org/Vol-2482/paper20.pdf
|volume=Vol-2482
|authors=Ruggero G. Pensa
|dblpUrl=https://dblp.org/rec/conf/cikm/Pensa18
}}
==Enhancing Privacy Awareness in Online Social Networks: a Knowledge-Driven Approach==
https://ceur-ws.org/Vol-2482/paper20.pdf
Enhancing Privacy Awareness in Online Social Networks:
a Knowledge-Driven Approach
Ruggero G. Pensa
University of Turin, Dept. of Computer Science
Turin, Italy I-10149
ruggero.pensa@unito.it
Extended abstract vacy breach event has multiplied the interests in the
protection of human dignity and personal data, and
Online social networks are permeating most aspects privacy has become a primary concern among social
of our life. More than two billions active social ac- network providers and web/data scientists.
counts are producing petabytes of behavioral and in-
teraction data daily. At the same time, the famous Although social platforms often provide some kind
“six degrees of separation” theory has been far exceed of notification intended to inform their users about
in Facebook, where an average degree of 3.57 has been the risks of private information disclosure, many peo-
recently observed. This massive interconnection in- ple simply overlook the dangers due to the uncon-
trinsically exposes social network users to the risk of trolled disclosure of their (and others’) personal data.
privacy leakage. Therefore, following the recent scandals, most social
If, from one hand, many users are informed about media have considerably improved their tools for con-
the risks linked to the disclosure of sensitive informa- trolling the privacy settings of the user profile (e.g.,
tion (private life events, sexual preferences, diseases, Instagram can now limits the visibility of stories to
political ideas, among others), on the other hand the “close-friend”), but such tools are often hidden and
awareness of being exposed to privacy breaches each not that user-friendly. Consequently, they are barely
time we disclose information that apparently is not utilized by most users. Recent machine learning and
sensitive is still insufficiently widespread. In this re- data mining studies try to go beyond these limitations
gard, daily activities may reveal information that can by proposing some measures of users’ profile privacy
be used by others in a negative manner. For example, based on the way they customize their privacy set-
a GPS tag far from home or pictures taken during a tings, or lightening the customization process of the
journey may alert potential burglars, or the disclosure privacy settings by means of guided tools and wiz-
of family relationships may expose our own or other ards [FL10, SWN+ 18]. Privacy measures, in partic-
family members’ privacy to criminal offence risks, as ular, when associated to popup alerts or other visual
well as source of tort liability. Most troubling of all, it components, may enhance user’s perception of privacy,
has been shown that by leveraging Facebook user’s ac- according to the principles of Privacy by Design spec-
tivity it is possible to infer some very private traits of ifications [Cav12]. These metrics usually require a
the user’s personality [KSG13]. This inference capabil- separation-based policy configuration: in other terms,
ity has been recently exploited to help propel Donald the users decide “how distant” a published item may
Trump to victory in the last U.S. presidential elec- spread in the network. Typical separation-based pri-
tions and was at the very center of the Facebook– vacy policies for profile item/post visibility include:
Cambridge Analytica scandal in early 2018. This pri- visible to no one, visible to friends, visible to friends
of friends, public. However, this policy fails when the
Copyright © CIKM 2018 for the individual papers by the papers' number of user friends becomes large. According to a
authors. Copyright © CIKM 2018 for the volume as a collection well-known anthropological theory, in fact, the maxi-
by its editors. This volume and its papers are published under mum number of people with whom one can maintain
the Creative Commons License Attribution 4.0 International (CC stable social (and cybersocial) relationships (known as
BY 4.0).
Dunbar’s number) is around 150, but the average num-
ber of user friends in Facebook is more than double.
This means that many social links are weak (offline
and online interactions with them are sporadic), and simulated networks but also on a large network of real
a user who sets the privacy level of an item to “visible Facebook users.
to friends” probably is not willing to make that item
visible to all her friends. Other studies try to make Acknowledgements
the customization process of the privacy settings less The work presented in the talk is supported by Fon-
frustrating. However, a consensus on how to identify dazione CRT (grant numbers 2015-1638 and 2017-
a trade-off between privacy protection and exploita- 2323).
tion of social network potentials is still far from being
achieved.
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