Proximity-based Mobile Social Networks Research Articles

Privacy-preserving multi-hop profile-matching protocol for proximity mobile social networks

Proximity-based mobile social networks (PMSNs) enable users to easily discover and foster social interactions with others through user-profile matching. The user profiles in PMSNs contain sensitive personal information and an occasional leak will violate people’s privacy. Hence, it is a major concern. In this paper, we propose a privacy-preserving multi-hop profile-matching protocol for PMSNs. The proposed protocol allows users to customize their own matching preference and to make the matching results more precise. Unlike the state-of-the-art profile matching approaches that focus only within a single-hop, the proposed approach makes profile matching within several hops. Moreover, analysis of the security and performance indicates that the proposed protocol achieves secure and privacy-preserving friend discovery with higher efficiency. It executes in less time and consumes less energy than other related protocols.

Message in a Sealed Bottle: Privacy Preserving Friending in Mobile Social Networks

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants’ profiles and communications. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized mobile social networks. Meanwhile, our mechanisms establish a secure communication channel between the initiator and matching users at the time when a matching user is found. These techniques can also be applied to conduct privacy preserving keywords based search without any secure communication channel. Our analysis shows that our mechanism is privacy-preserving (no participants’ profile and the submitted preference-profile are exposed), verifiable (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.

Privacy-preserving Attribute Matchmaking in Proximity-based Mobile Social Networks

Privacy-preserving Attribute Matchmaking in Proximity-based Mobile Social Networks

Privacy-Preserving Distributed Profile Matching in Proximity-Based Mobile Social Networks

Making new connections according to personal preferences is a crucial service in mobile social networking, where an initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, a set of privacy-preserving profile matching schemes for proximity-based mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Two increasing levels of user privacy are defined, with decreasing amounts of revealed profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize each of the user privacy levels, which can also be personalized by the users. We provide formal security proofs and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.