NDLI: Collective privacy management in social networks

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Collective privacy management in social networks

Content Provider	ACM Digital Library
Author	Shehab, Mohamed Paci, Federica Squicciarini, Anna Cinzia
Abstract	Social Networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expression for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who promote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users.
Starting Page	521
Ending Page	530
Page Count	10
File Format	PDF
ISBN	9781605584874
DOI	10.1145/1526709.1526780
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-04-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Privacy Social networks Game theory
Content Type	Text
Resource Type	Article

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