Abstract
Scalar product protocol aims at securely computing the dot product of two private vectors. As a basic tool, the protocol has been widely used in privacy preserving distributed collaborative computations. In this paper, at the expense of disclosing partial sum of some private data, we propose a linearly efficient Even-Dimension Scalar Product Protocol (EDSPP) without employing expensive homomorphic crypto-system and third party. The correctness and security of EDSPP are confirmed by theoretical analysis. In comparison with six most frequently-used schemes of scalar product protocol (to the best of our knowledge), the new scheme is a much more efficient one, and it has well fairness. Simulated experiment results intuitively indicate the good performance of our novel scheme. Consequently, in the situations where divulging very limited information about private data is acceptable, EDSPP is an extremely competitive candidate secure primitive to achieve practical schemes of privacy preserving distributed cooperative computations. We also present a simple application case of EDSPP.
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References
HIPAA. The health insurance portability and accountability act of 1996 (October 1998), http://www.ocius.biz/hipaa.html
Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1-2), 1–24 (2002)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM Sigmod Record 29, 439–450 (2000)
Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. Journal of Privacy and Confidentiality 1(1), 59–98 (2009)
Yang, B., Nakagawa, H., Sato, I., Sakuma, J.: Collusion-resistant privacy-preserving data mining. In: The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 483–492 (2010)
Chen, T., Zhong, S.: Privacy-preserving backpropagation neural network learning. IEEE Transactions on Neural Networks 20(10), 1554–1564 (2009)
Bansal, A., Chen, T., Zhong, S.: Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data. Neural Computing and Applications 20(1), 143–150 (2011)
Murugesan, M., Jiang, W., Clifton, C., Si, L., Vaidya, J.: Efficient privacy-preserving similar document detection. The VLDB Journal 19(4), 457–475 (2010)
Xiao, M., Huang, L., Xu, H., Wang, Y., Pei, Z.: Privacy Preserving Hop-distance Computation in Wireless Sensor Networks. Chinese Journal of Electronics 19(1), 191–194 (2010)
Zhu, Y., Huang, L., Dong, L., Yang, W.: Privacy-preserving Text Information Hiding Detecting Algorithm. Journal of Electronics and Information Technology 33(2), 278–283 (2011)
Smaragdis, P., Shashanka, M.: A framework for secure speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 15(4), 1404–1413 (2007)
Du, W., Zhan, Z.: A practical approach to solve secure multi-party computation problems. In: The 2002 Workshop on New Security Paradigms, pp. 127–135. ACM, New York (2002)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: The 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644. ACM, New York (2002)
Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On Private Scalar Product Computation for Privacy-Preserving Data Mining. In: Park, C.-s., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, pp. 104–120. Springer, Heidelberg (2005)
Amirbekyan, A., Estivill-Castro, V.: A new efficient privacy-preserving scalar product protocol. In: The Sixth Australasian Conference on Data Mining and Analytics, vol. 70, pp. 209–214. Australian Computer Society (2007)
Shaneck, M., Kim, Y.: Efficient Cryptographic Primitives for Private Data Mining. In: The 2010 43rd Hawaii International Conference on System Sciences, pp. 1–9. IEEE Computer Society (2010)
Goldreich, O.: Foundations of Cryptography: Volume II, Basic Applications. Cambridge University Press, Cambridge (2004)
Qi, Y., Atallah, M.J.: Efficient privacy-preserving k-nearest neighbor search. In: The 28th International Conference on Distributed Computing Systems (ICDCS 2008), pp. 311–319. IEEE (2008)
Shaneck, M., Kim, Y., Kumar, V.: Privacy preserving nearest neighbor search. In: 6th IEEE International Conference on Data Mining Workshops, pp. 541–545. IEEE (2006)
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Zhu, Y., Takagi, T., Huang, L. (2012). Efficient Secure Primitive for Privacy Preserving Distributed Computations. In: Hanaoka, G., Yamauchi, T. (eds) Advances in Information and Computer Security. IWSEC 2012. Lecture Notes in Computer Science, vol 7631. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34117-5_15
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DOI: https://doi.org/10.1007/978-3-642-34117-5_15
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