Abstract
With the advent of big data era, it’s becoming an increasing trend for different clients lack of computational resources to cooperate in outsourcing data mining tasks to cloud service providers in order to produce maximum value of the joint database. Generally, the outsourced data contributed by clients should be encrypted under different keys owing to security concerns. Unfortunately, existing privacy-preserving outsourcing protocols are either restricted to a single key setting or quite inefficient due to frequent server-client interactions, making the deployment far from practical. In this paper, we focus on outsourced k-Nearest Neighbor (kNN) classification over encrypted data under multiple keys, and propose a set of secure building blocks and the Secure Collaborative Outsourced kNN (SCOkNN) protocol. Theoretical analysis shows that the proposed protocol protects the confidentiality of data from data owners, privacy of query, and access patterns in the semi-honest model with negligible computation and communication costs. Experimental evaluation also demonstrates its practicability and efficiency.
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References
Wong, W.K., Cheung, D.W., Kao, B., Mamoulis, N.: Secure kNN computation on encrypted database. In: SIGMOD, pp. 139–152 (2009)
Xu, H., Guo, S., Chen, K.: Building confidential and efficient query services in the cloud with RASP data perturbation. IEEE Trans. Knowl. Data Eng. 26(2), 322–335 (2014)
Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: ICDE, pp. 664–675 (2014)
Samanthula, B.K., Elmehdwi, Y., Jiang, W.: K-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1–14 (2015)
Li, F., Shin, R., Paxson, V.: Exploring privacy preservation in outsourced K-nearest neighbors with multiple data owners. In: CCSW 2015, pp. 53–64 (2015)
Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)
Wang, B., Li, M., Chow, S.S.M., Li, H.: Computing encrypted cloud data efficiently under multiple keys. In: 4th International Workshop on Security and Privacy in Cloud Computing, pp. 504–513 (2013)
Wang, B., Li, M., Chow, S.S.M., Li, H.: A tale of two clouds: computing on data encrypted under multiple keys. In: IEEE CNS, pp. 337–345 (2014)
Zhu, Y., Xu, R., Takagi, T.: Secure k-NN computation on encrypted cloud data without sharing key with query users. In: ACM Cloud Computing, pp. 55–60 (2013)
Chow, S.S.M., Lee, J.H., Strauss, M.: Two-party computation model for privacy-preserving queries over distributed databases. In: NDSS (2009)
Van, M.D., Juels, A.: On the impossibility of cryptography alone for privacy-preserving cloud computing. In: HotSec 2010, pp. 1–8 (2010)
Blaze, M., Bleumer, G., Strauss, M.: Divertible protocols and atomic proxy cryptography. In: Nyberg, K. (ed.) EUROCRYPT 1998. LNCS, vol. 1403, pp. 127–144. Springer, Heidelberg (1998). doi:10.1007/BFb0054122
Weng, J., Deng, R.H., Liu, S., Chen, K.: Chosen-ciphertext secure bidirectional proxy re-encryption schemes without pairings. Inf. Sci. 180, 5077–5089 (2010)
Canetti, R., Hohenberger, S.: Chosen-ciphertext secure proxy re-encrytpion. In: CCS 2007, pp. 185–194 (2007)
Goldreich, O.: The Foundations of Cryptography: Volume 2, Basic Applications, pp. 600–759. Cambridge University Press, Cambridge (2004)
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: The UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets/Wine+Quality
Huang, Y., Lu, Q., Xiong, Y.: Collaborative outsourced data mining for secure cloud computing. J. Netw. 9(10), 2655–2664 (2014)
Rong, H., Wang, H., Huang, K., Liu, J., Xian, M.: Privacy-preserving scalar product computation in cloud environments under multiple keys. In: Yin, H., Gao, Y., Li, B., Zhang, D., Yang, M., Li, Y., Klawonn, F., Tallón-Ballesteros, A.J. (eds.) IDEAL 2016. LNCS, vol. 9937, pp. 248–258. Springer, Cham (2016). doi:10.1007/978-3-319-46257-8_27
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Rong, H., Wang, H., Liu, J., Wu, W., Hao, J., Xian, M. (2017). Secure Collaborative Outsourced k-Nearest Neighbor Classification with Multiple Owners in Cloud Environment. In: Chen, K., Lin, D., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2016. Lecture Notes in Computer Science(), vol 10143. Springer, Cham. https://doi.org/10.1007/978-3-319-54705-3_28
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DOI: https://doi.org/10.1007/978-3-319-54705-3_28
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