Do I know you?

Abstract

The increasing penetration of Online Social Networks (OSNs) prompts the need for effectively accessing and utilizing social networking information. In numerous applications, users need to make trust and/or access control decisions involving other (possibly stranger) users, and one important factor is often the existence of common social relationships. This motivates the need for secure and privacy-preserving techniques allowing users to assess whether or not they have mutual friends.This paper introduces the Common Friends service, a framework for finding common friends which protects privacy of nonmutual friends and guarantees authenticity of friendships. First, we present a generic construction that reduces to secure computation of set intersection, while ensuring authenticity of announced friends via bearer capabilities. Then, we propose an efficient instantiation, based on Bloom filters, that only incurs a constant number of public-key operations and appreciably low communication overhead. Our software is designed so that developers can easily integrate Common Friends into their applications, e.g., to enforce access control based on users' social proximity in a privacypreserving manner. Finally, we showcase our techniques in the context of an existing application for sharing (tethered) Internet access, whereby users decide to share access depending on the existence of common friends. A comprehensive experimental evaluation attests to the practicality of proposed techniques.

A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced Scenarios

“…Encrypted randomized Bloom filters (ERBF): The encrypted randomized Bloom filter is denoted as ERBF, where ERBF = {Enc(RBF [1]), Enc(RBF [2]), . .…”

“…Private Set Intersection (PSI) addresses the current need for a solution to compute the intersection of two or more datasets without disclosing information about the data of each party, except for the intersection itself. PSI has wide applications such as social discovery [1][2][3], document-like detection [4], joint learning in neural network models [5], suspect detection [6], privacy-preserving data mining [7], privacy-preserving retrieval systems [8], cloud-based applications [9], and so on. Many high-performance PSI protocols have been proposed recently [10][11][12][13][14][15][16].…”

Private Collection Matching Protocols

“…In PSI, clients learn information about the intersection of two sets while (i) not learning anything about the server's non-intersecting elements, and (ii) not leaking any information about their own set to the server. PSI protocols in the literature focus on providing two possible outputs: the intersection (e.g., finding common network intrusions [49], or discovering contacts [19]); and the cardinality of the intersection (e.g., privately counting the number of common friends between two social media users [50], or performing genomic tests [4]). Works in this area opt for a variety of trade-offs between the computational capability and the amount of bandwidth required to run the protocol [11,34,37,54].…”