95,288 research outputs found
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
ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs
The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal
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