Secure friend discovery in mobile social networks

Abstract

Abstract-Mobile social networks extend social networks in the cyberspace into the real world by allowing mobile users to discover and interact with existing and potential friends who happen to be in their physical vicinity. Despite their promise to enable many exciting applications, serious security and privacy concerns have hindered wide adoption of these networks. To address these concerns, in this paper we develop novel techniques and protocols to compute social proximity between two users to discover potential friends, which is an essential task for mobile social networks. We make three major contributions. First, we identify a range of potential attacks against friend discovery by analyzing real traces. Second, we develop a novel solution for secure proximity estimation, which allows users to identify potential friends by computing social proximity in a privacy-preserving manner. A distinctive feature of our solution is that it provides both privacy and verifiability, which are frequently at odds in secure multiparty computation. Third, we demonstrate the feasibility and effectiveness of our approaches using real implementation on smartphones and show it is efficient in terms of both computation time and power consumption.

A Survey of Social Network Forensics

“…OSN service providers should help to find trustable friends. Dong et al (2011) provide a method of finding secure friends by letting users utilize virtual IDs and digital signatures for authentication, applying proximity prefiltering by eliminating profiles that are less likely to be friends and enhancing this scheme with homomorphic cryptography to validate social coordinates and proximity results.…”

Verifiable private multi-party computation: Ranging and ranking

“…Each dot product result gives one linear constraint on v k . In a traditional way as stated in [2], an adversary needs M linearly independent constraints to reconstruct the victim's private vector v k . Indeed, v k is usually K-sparse (it has at most K non-zeros) and K ≪ M .…”

“…Encrypted private values are input, and a series of computation are conducted homomorphically on these encrypted values to generate the encryption of the computation result on these values. A few methods like [2] propose to use a signature of encrypted input from a trusted third party P T to ensure the authenticity and consistency of the input value. However, we find that if the trusted third party P T directly signs the encrypted value E i (v i ) and the digital signature generation system are homomorphic, the party P i is able to generate a fake signature for value k · v i or v k i without contacting P T .…”

“…The true result is only revealed to one party, who can cheat other parties by a forge result. Dong et al [2] propose the fist scheme supporting verifiable private dot product between two parties.…”

“…The correctness of most protocols is based on the assumption that the involved parties will follow the protocol honestly. In practice a malicious adversary may simply forge his/her input value to produce an incorrect result or lie about the outcome of computation [2]. In this way, the malicious adversary can cheat other parties to accept the incorrect result and even compromise other parties' privacy.…”