Abstract-Location Based Services (LBS) are becoming increasingly popular. Users enjoy a wide range of services from tracking a lost phone to querying for nearby restaurants or nearby tweets. However, many users are concerned about sharing their location. A major challenge is achieving the privacy of LBS without hampering the utility. This paper focuses on the problem of location proximity, where principals are willing to reveal whether they are within a certain distance from each other. Yet the principals are privacy-sensitive, not willing to reveal any further information about their locations, nor the distance. We propose InnerCircle, a novel secure multi-party computation protocol for location privacy, based on partially homomorphic encryption. The protocol achieves precise fully privacy-preserving location proximity without a trusted third party in a single round trip. We prove that the protocol is secure in the semihonest adversary model of Secure Multi-party Computation, and thus guarantees the desired privacy properties. We present the results of practical experiments of three instances of the protocol using different encryption schemes. We show that, thanks to its parallelizability, the protocol scales well to practical applications.
Hallgren et al. REF develop a PPLP protocol built on only AHPKE to test whether two users' locations are within a given distance threshold (without a trusted third party).
InnerCircle: A parallelizable decentralized privacy-preserving location proximity protocol
2015 13th Annual Conference on Privacy, Security and Trust (PST)
2015 13th Annual Conference on Privacy, Security and Trust (PST)
Computer Science