Privacy and spectral analysis of social network randomization

Social networks are of significant importance in various application domains. Un- derstanding the general properties of real social networks has gained much attention due to the proliferation of networked data. Many applications of networks such as anonymous web browsing and data publishing require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of the relationship. One general ap- proach for this problem is to randomize the edges in true networks, and only release the randomized networks for data analysis. Our research focuses on the development of randomization techniques such that the released networks can preserve data utility while preserving data privacy. Data privacy refers to the sensitive information in the network data. The released network data after a simple randomization could incur various disclosures including identity disclosure, link disclosure and attribute disclosure. Data utility refers to the information, features, and patterns contained in the network data. Many important features may not be preserved in the released network data after a simple randomiza- tion. In this dissertation, we develop advanced randomization techniques to better preserve data utility of the network data while still preserving data privacy. Specifi- cally we develop two advanced randomization strategies that can preserve the spectral properties of the network or can preserve the real features (e.g., modularity) of the network. We quantify to what extent various randomization techniques can protect data privacy when attackers use different attacks or have different background knowl- edge. To measure the data utility, we also develop a consistent spectral framework to measure the non-randomness (importance) of the edges, nodes, and the overall graph. Exploiting the spectral space of network topology, we further develop fraud detection techniques for various collaborative attacks in social networks. Extensive theoretical analysis and empirical evaluations are conducted to demonstrate the efficacy of our developed techniques

Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Preserving Co-Location Privacy in Geo-Social Networks

The number of people on social networks has grown exponentially. Users share very large volumes of personal informations and content every days. This content could be tagged with geo-spatial and temporal coordinates that may be considered sensitive for some users. While there is clearly a demand for users to share this information with each other, there is also substantial demand for greater control over the conditions under which their information is shared. Content published in a geo-aware social networks (GeoSN) often involves multiple users and it is often accessible to multiple users, without the publisher being aware of the privacy preferences of those users. This makes difficult for GeoSN users to control which information about them is available and to whom it is available. Thus, the lack of means to protect users privacy scares people bothered about privacy issues. This paper addresses a particular privacy threats that occur in GeoSNs: the Co-location privacy threat. It concerns the availability of information about the presence of multiple users in a same locations at given times, against their will. The challenge addressed is that of supporting privacy while still enabling useful services.Comment: 10 pages, 5 figure

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system