Abstract
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.
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