Abstract
Location based services (LBS), as an important part of people’s everyday life, rely on current/historical location information to offer services. However, malicious users may generate some fake information to cheat providers to obtain more profits. Currently, existing schemes directly leverage GPS or a centralized party to claim location information, which may be easily counterfeited or result in leakage of users’ privacy. To authenticate the location information, an interactive scheme can be introduced to allow a mobile user as prover to generate proofs of location by exploiting neighboring nodes as witnesses. However, owing to the selfishness of users, it is a great challenge to encourage witnesses to generate proofs. Moreover, witnesses will not be willing to generate proofs unless that their privacy is under well protection. To tackle the above issues, we propose a blockchain-based incentive mechanism with privacy preserving in location proof. First, a novel blockchain system is introduced for interactive location proof to generate secure and trustworthy proofs through the use of distributed ledgers and cryptocurrency. Second, a proof of points protocol is proposed to efficiently reach consensus to build location proof blockchain and incentivise users. Third, motivated by security problem, a novel concept of traceable-detectable-prefix is developed to resist collusion attacks while protecting users’ privacy. Finally, theoretical analysis and simulation experiments are provided, which demonstrate that the proposed scheme can provide complete location proof while protecting privacy of users.
This work is supported by the National Key Research and Development Program of China (No. 2018YFB0204301), NUDT Research Grants (No. ZK19-38), Open Foundation of State Key Laboratory of Cryptology (No: MMKFKT201617), and National Nature Science Foundation of China (grant 61572026, 61872372).
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Lin, Z., Luo, Y., Fu, S., Xie, T. (2020). BIMP: Blockchain-Based Incentive Mechanism with Privacy Preserving in Location Proof. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12454. Springer, Cham. https://doi.org/10.1007/978-3-030-60248-2_35
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