Abstract
Nowadays, with the popularity of location-aware devices, multifarious applications based on the spatio-temporal data come forth in our lives. In these applications, a platform (enterprise) collects the users’ spatio-temporal data based on which it recommends the top-k users (passengers) to the registered service providers (drivers). Outsourcing the tremendous scale of spatio-temporal data to cloud provides an economical way for the enterprises to implement their applications. In this paradigm, the third-party cloud server is not completely trustworthy. The collected spatio-temporal data can hold users’ privacy, so it’s a critical challenge to design a secure and efficient query mechanism for this scenario, such as the ride-hailing or the ride-sharing services. However, the existing solutions for the privacy-preserving kNN queries mainly focus on data privacy protection or computation complexity. There still lacks a practical privacy-preserving polynomial evaluation solution over the spatio-temporal data. In this paper, we propose a virtual road network structure to storage and index the spatio-temporal data in the road network and design a novel homomorphic encryption scheme based on Order-Revealing Encryption to enable an untrusted cloud server to execute the polynomial evaluation over the encrypted spatio-temporal data in the road network. We formally prove the security of the proposed scheme under the random oracle model. Extensive experiments on real world data demonstrate the effectiveness and efficiency of the proposed scheme over alternatives.
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References
Wong, W.K., Cheung, D.W., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted databases. In: SIGMOD, pp. 139–152 (2009)
Yao, B., Li, F., Xiao, X.: Secure nearest neighbor revisited. In: ICDE, pp. 733–744 (2013)
Choi, S., Ghinita, G., Lim, H.S., Bertino, E.: Secure kNN query processing in untrusted cloud environments. TKDE 26(11), 2818–2831 (2014)
Cui, N., Yang, X., et al.: SVkNN: efficient secure and verifiable k-nearest neighbor query on the cloud platform. In: ICDE, pp. 253–264 (2020)
Lei, X., Liu, A.X., Li, R., Tu, G.-H.: SecEQP: a secure and efficient scheme for SkNN query problem over encrypted geodata on cloud. In: ICDE (2019)
Rodrigo, A., Dayarathna, M., Jayasena, S.: Latency-aware secure elastic stream processing with homomorphic encryption. Data Sci. Eng. 4(3), 223–239 (2019). https://doi.org/10.1007/s41019-019-00100-5
Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: ICDE, pp. 664–675 (2014)
Palanisamy, B., Liu, L.: MobiMix: protecting location privacy with mix-zones over road networks. In: ICDE, pp. 494–505 (2011)
Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical approximate k nearest neighbor queries with location and query privacy. TKDE 28(6), 1546–1559 (2016)
Paulet, R., Kaosar, M.G., Yi, X., Bertino, E.: Practical approximate k nearest neighbor queries with location and query privacy. TKDE 26(5), 1200–1210 (2014)
Yang, S., Tang, S., Zhang, X.: Privacy-preserving k nearest neighbor query with authentication on road networks. JPDC 134, 25–36 (2019)
Zeng, M., Zhang, K., Chen, J., Qian, H.: P3GQ: a practical privacy-preserving generic location-based services query scheme. PMC 51, 56–72 (2018)
Pham, A., Dacosta, I., et al.: PrivateRide: a privacy-enhanced ride-hailing service. Priv. Enhancing Technol. 2017(2), 38–56 (2017)
Pham, A., Dacosta, I., et al. ORide: a privacy-preserving yet accountable ride-hailing service. In: USENIX Security, pp. 1235–1252 (2017)
Wang, F., Zhu, H., et al.: Efficient and privacy-preserving dynamic spatial query scheme for ride-hailing services. IEEE Trans. Veh. Technol. 67(11), 11084–11097 (2018)
Sherif, A., Rabieh, K., et al.: Privacy-preserving ride sharing scheme for autonomous vehicles in big data era. IEEE Internet Things J. 4(2), 611–618 (2016)
Li, M., Zhu, L., Lin, X.: Efficient and privacy-preserving carpooling using blockchain-assisted vehicular fog computing. IEEE Internet Things J. 6(3), 4573–4584 (2018)
Song, W., Wang, B., Wang, Q., Shi, C., Lou, W., Peng, Z.: Publicly verifiable computation of polynomials over outsourced data with multiple sources. TIFS 12(10), 2334–2347 (2017)
Xu, Y., Tong, Y., Shi, Y., Tao, Q., Xu, K., Li, W.: An efficient insertion operator in dynamic ridesharing services. In: TKDE (2020)
Nabil, M., Sherif, A., et al.: Efficient and privacy-preserving ridesharing organization for transferable and non-transferable services. TDSC PP, 1 (2019)
Meng, X., Zhu, H., Kollios, G.: Top-k query processing on encrypted databases with strong security guarantees. In: ICDE, pp. 353–364 (2018)
Song, W., Shi, C., Shen, Y., Peng, Z.: Select the best for me: privacy-preserving polynomial evaluation algorithm over road network. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11447, pp. 281–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18579-4_17
Lewi, K., Wu. D.J.: Order-revealing encryption: new constructions, applications, and bounds. In: CCS, pp. 1167–1178 (2016)
Goldreich, O., Goldwasser, S., Micali, S.: How to construct random functions. J. ACM 33(4), 792–807 (1986)
Samanthala, B.K., Chun, H., Jiang, W.: An efficient and probabilistic secure bit-decomposition. In: AsiaCCS, pp. 541–546 (2013)
Acknowledgements
This work is partially supported by National Key Research and Development Project of China Nos. 2020YFC1522602, 2020AAA0107700, National Natural Science Foundation of China Nos. 62072349, U1811263, 61572378, 61822207, U20B2049, Technological Innovation Major Program of Hubei Province No. 2019AAA072, JSPS KAKENHI No.19K20269, and CCF-Tencent Open Fund WeBank Special Fund.
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Song, W. et al. (2021). Privacy-Preserving Polynomial Evaluation over Spatio-Temporal Data on an Untrusted Cloud Server. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12681. Springer, Cham. https://doi.org/10.1007/978-3-030-73194-6_32
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DOI: https://doi.org/10.1007/978-3-030-73194-6_32
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