Social Networking Security and Privacy
Strangers Intrusion Detection - Detecting Spammers and Fake Profiles in Social Networks Based on Topology Anomalies
by Michael Fire
Co-authored Gilad Katz, Yuval Elovici
Draft Version
Today’s social networks are plagued by numerous types
of malicious profiles, ranging from bots to sexual... more
Today’s social networks are plagued by numerous types
of malicious profiles, ranging from bots to sexual predators. We present a novel method for the detection of these
malicious profiles by only using the social network’s own
topological features. The reliance on only these features
ensures that the proposed method is generic enough to
be applied on many types of social networks. The algorithm has been evaluated on several social networks and
was found to be effective in detecting several types of
malicious profiles. We believe this method is an important step towards making social networks less vulnerable to spammers, socialbots and sexual predators.
Complexity of Social Network Anonymization
by Sean Chester
Ssocial network privacy paper co-authored with Bruce M. Kapron, Gautam Srivastava, and Venkatesh Srinivasan. To appear in an upcoming issue of the Springer journal Social Network Analysis and Mining (SNAM).
With an abundance of social network data being released, the need
to protect sensitive information within these... more
With an abundance of social network data being released, the need
to protect sensitive information within these networks has become an impor-
tant concern of data publishers. To achieve this objective, various notions of
k-anonymization have been proposed for social network graphs. In this paper
we focus on the complexity of optimization problems that arise from trying
to anonymize graphs, establishing that optimally k-anonymizing the label se-
quences of edge-labeled graphs is intractable. We show how this result implies
intractability for other notions of k-anonymization in literature.
We also consider the case of bipartite social network graphs which arise
from the representation of distinct entities, such as movies and viewers, pa-
tients and drugs, or products and customers. Within this setting we demon-
strate that, although k-anonymizing edge-labeled graphs is intractable for
k ≥ 3, polynomial time algorithms exist for arbitrary bipartite graphs when
k = 2 and for unlabeled bipartite graphs irrespective of the value of k.
Finally, in this paper we extend the study of attribute disclosure within
the context of social networks by defining t-closeness, a measure of how effec-
tively an adversary can determine sensitive information about members of a
k-anonymous social network.
Secure Management of Social Networks Applications Data
Co-authored with Jaime Delgado and Eva Rodríguez
Published in VirtualGoods 2010 Proceedings
The number of users of online social networks has increased dramatically during the last years. In turn, the number of... more The number of users of online social networks has increased dramatically during the last years. In turn, the number of applications offered by these sites, as well as their usage by social networks users has also increased significantly. These applications, developed by third parties, access users data in order to properly work. This fact poses serious privacy risks for users, since social networking sites don’t provide them mechanisms to specify their privacy preferences for the usage done by third party applications over their personal data. This paper proposes a solution based on the usage of rights expression languages to control the usage done by social networks applications of users’ personal data.
Links Reconstruction Attack Using Link Prediction Algorithms to Compromise Social Networks Privacy
by Michael Fire
Co-authored with Gilad Katz, Lior Rokach and Yuval Elovici
The explosion in the use of social networks has led to the creation of new kinds of security and privacy threats. Many... more The explosion in the use of social networks has led to the creation of new kinds of security and privacy threats. Many users are unaware of the risks involved with exposing personal information, a fact that makes social networks a “bonanza” for spammers and identity thieves. In addition, it has already been proven that even the concealment of all personal data might not be enough to provide protection, as one’s personal information can be inferred by analyzing one’s connections to other users. In this paper we present the “link reconstruction attack”, a method capable of inferring one’s connections to others with high accuracy. We show that the concealment of ones links is ineffective if not done by others in the network and present an analysis of the performance of various machine learning algorithms for link prediction inside communities.
Türkiye'de Tumblr Blog Ortamındaki Popüler Blogcuların Kullanım Pratikleri
by Tuğçe Erbaş
inet’11 Internet Conference in Turkey (30th November – 2nd December 2011, Izmir); presented at session on “New Media”.
Along with the advancement of information and communication technologies, new media has become more of an issue as a... more Along with the advancement of information and communication technologies, new media has become more of an issue as a space where people present themselves intensely. It is also observed that blogs are the important communication tools which are prefered to use by most of the Internet users on new media. In this thesis, the usage practices of popular bloggers who perform self-presentation on blogs in new media are discussed through the findings of the content analysis and the in-depth interviews. The findings are interpreted by the facts such as intimacy, self-presentation on virtual space and the desire of being seen which are the subjects of new media studies substantially. This thesis, which Tumblr blogware is formed as research site, has brought out the fact that the reasons of the desire of being on blogs, the points and the forms of usage are depend on the individuals. It also brings into open some mutual usage practices of popular bloggers.
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Seen by:k-Anonymization of Social Networks by Vertex Addition
by Sean Chester
ADBIS 2011 short paper co-authored with Gautam Srivastava, Bruce Kapron, Ganesh Ramesh, Alex Thomo, and Venkatesh Srinivasan.
With an abundance of social network data being released, the need to protect sensitive information within these... more With an abundance of social network data being released, the need to protect sensitive information within these networks has become an important concern of data publishers. In this paper we focus on the popular notion of k-anonymization as applied to node degrees in a social network. Given such a network N , the problem we study is to transform N to N ′ , such that the degree of each node in N ′ is attained by at least k − 1 other nodes in N ′ . Apart from previous work, we permit modifications to the node set, rather than the edge set, and this offers unique advantages with respect to the utility of the released anonymized network. We study both vertex-labeled and unlabeled graphs, since instances of each occur in real-world social networks. Under the constraint of minimum node additions, we show that on vertex-labeled graphs, the problem is NP-complete. For unlabeled graphs, we give an efficient (near-linear) algorithm and show that it gives solutions that are optimal modulo k, a guarantee that is novel in the literature. Additionally, we demonstrate empirically that commonly-studied structural properties of the network, such as clustering coefficient, are quite minorly distorted by the anonymization procedure.
Threat Risks Identification and a Control Method In Social Networks Security
Presented at KASPERSKY LAB ASIA PACIFIC & MEA CUP 2011
The students’ conference will be held at the University of Technology, Shah Alam, Malaysia from the March 4- 6, 2011
Social networks are attractive
websites in recent years with increasing
number of users and also rising... more
Social networks are attractive
websites in recent years with increasing
number of users and also rising privacy
and security issues. In this article the
emerging threats for security of social
networks have been discussed. We found
threat risks and measured each one; the
software’s theory for helping social
networks users has also been presented.
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Seen by:Social Network Privacy for Attribute Disclosure Attacks
by Sean Chester
Short paper co-authored with Gautam Srivastava to appear at ASONAM 2011.
Increasing research on social networks stresses the urgency for producing effective means of ensuring user... more
Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected.
To address this gap, we introduce an approach to anonymize social networks that have labeled nodes, alpha-nearness, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve alpha-nearness and experimentally validate the quality of the solutions it derives.

