Browsing by Author "KARRAS, Panagiotis"

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    Discretionary Social Network Data Revelation with a User-Centric Utility Guarantee
    (2012-08-08) YI, Song; KARRAS, Panagiotis; NOBARI, Sadegh; CHELIOTIS, Giorgos; XUE, Mingqiang; BRESSAN, Stéphane
    The proliferation of online social networks has created intense interest in studying the nature of such networks and revealing network information of interest to the end user. At the same time, the revelation of such data raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on what constitutes a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: instead of starting out with a privacy objective, we define a utility guarantee, in terms of certain graph connectivity properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the whole network graph, in which that user as well as distant connections and affliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real-world data, we demonstrate that our method preserves graph properties of interest (e.g., clustering coefficient, shortest path length, diameter, radius) more successfully than methods that randomly distort the graph to an equal extent, while it withstands structural attacks proposed in the literature.
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    Sensitive Label Privacy Protection on Social Network Data
    (2012-03-30) SONG, Yi; KARRAS, Panagiotis; XIAO, Qian; BRESSAN, Stephane
    The publication of social network data presents opportunities for data mining and analytics for strategic public, commercial and academic applications. Yet the publication of social network data entails a privacy threat for their users. Sensitive information should be protected. The challenge is to devise methods to publish these data in a form that affords utility without compromising privacy. Previous research has proposed various privacy models with the corresponding protection mechanisms. These early privacy models are mostly concerned with identity and link disclosure. The social networks are modeled as graphs in which users are nodes. The threat definitions and the protection mechanisms leverage structural properties of the graph. This paper is motivated by the recognition of the need for a finer grain and more personalized privacy. We propose a privacy protection scheme that not only prevents the disclosure of identity of users but also the disclosure of selected features in users' profiles. An individual user can select which features of her profile she wishes to conceal. The social networks are modeled as graphs in which users are nodes and features are labels. Labels are denoted either as sensitive or as non-sensitive. We treat node labels both as background knowledge an adversary may possess, and as sensitive information that has to be protected. We present privacy protection algorithms that allow for graph data to be published in a form such that an adversary who possesses information about a node's neighborhood cannot safely infer its identity and its sensitive labels. To this aim, the algorithms transform the original graph into a graph in which nodes are sufficiently indistinguishable. The algorithms are designed to do so while losing as little information and while preserving as much utility as possible. We evaluate empirically the extent to which the algorithms preserve the original graph's structure and properties. We show that our solution is effective, efficient and scalable while offering stronger privacy guarantees than those in previous research.