Link Privacy in Social Networks

Abstract

We consider a privacy threat to a social network in which the goal of an attacker is to obtain knowledge of a significant fraction of the links in the network. We formalize the typical social network interface and the information about links that it provides to its users in terms of lookahead. We consider a particular threat where an attacker subverts user accounts to get information about local neighborhoods in the network and pieces them together in order to get a global picture. We analyze, both experimentally and theoretically, the number of user accounts an attacker would need to subvert for a successful attack, as a function of his strategy for choosing users whose accounts to subvert and a function of lookahead provided by the network. We conclude that such an attack is feasible in practice, and thus any social network that wishes to protect the link privacy of its users should take great care in choosing the lookahead of its interface, limiting it to 1 or 2, whenever possible.

OSN Crawling Schedulers and Their Implications onk-Plexes Detection

“…, the authors evaluated how different parameters of the crawler algorithms affect crawling efficiency (defined previously in Ref. ) as well as the quality of collected data. Biases produced by certain schedulers can be avoided by selecting the proper scheduler algorithm as shown in Ref.…”

“…In Ref. , edge privacy is studied from the point of view of the number of compromised accounts needed to expose as much nodes as possible depending on the lookahead of the network. Lookahead is defined as the distance from which a user can see his friends' links.…”

“…However, similar attacks can be conducted even when some profiles are not public. It has been shown previously that the percentage of public profiles needed to obtain all relationships of a set of users is really low, so the described attacks could also work when private profiles are found in the network.…”

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“…In some of his later work [2,46], Motwani investigated frameworks and algorithms for data sanitization, query auditing and related problems.…”

Persona

“…Ahern et al [3] study Flickr to see how location information is leaked through users' photographs. Several studies [16,18,38] exploit the friend graph to infer characteristics about users. Persona resolves these issues by allowing users to precisely express the policies under which their data, including friend information, is encrypted and stored.…”