Abstract
Secure scalar product protocol has wide applications for privacy-preservation in collaborative computation. In this paper, we propose a new secure scalar product protocol, which does not employ any public-key encryption and third party. Compared to scalar product computation without privacy-preservation, our proposed scheme introduces no extra communication overheads and little extra computation cost. That is, the new scheme can achieve almost optimal running efficiency, and thus is much applicable to privacy-preservation for large-scale data in collaborative computation. Theoretical analysis and evaluation indicate the security and efficiency of our scheme.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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. Artif. Intell. Med. 26(1–2), 1–24 (2002)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. J. Cryptology 15(3), 177–206 (2002)
Goldreich, O.: Foundations of Cryptography: Volume II, Basic Applications. Cambridge University Press, Cambridge (2004)
Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Priv. Confidentiality 1(1), 59–98 (2009)
Xiao, M., Huang, L., Xu, H., Wang, Y., Pei, Z.: Privacy preserving hop-distance computation in wireless sensor networks. Chin. J. Electron. 19(1), 191–194 (2010)
Agrawal, R., Imieliski, T., Swami, T.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Yuan, X., Huang, L., Yang, W.: Privacy preserving computation of trust-value in wireless sensor networks. In: IEEE 3rd International Conference on Communication Software and Networks, pp. 573–576 (2011)
Murugesan, M., Jiang, W., Clifton, C., Si, L., Vaidya, J.: Efficient privacy-preserving similar document detection. VLDB J. 19(4), 457–475 (2010)
Chen, T., Zhong, S.: Privacy-preserving back-propagation neural network learning. IEEE Trans. Neural Netw. 20(10), 1554–1564 (2009)
Bansal, A., Chen, T., Zhong, S.: Privacy preserving back-propagation neural network learning over arbitrarily partitioned data. Neural Comput. Appl. 20(1), 143–150 (2011)
Zhu, Y., Huang, L., Dong, L., Yang, W.: Privacy-preserving text information hiding detecting algorithm. J. Electron. Inf. Technol. 33(2), 278–283 (2011)
Smaragdis, P., Shashanka, M.: A framework for secure speech recognition. IEEE Trans. Audio Speech Lang. Process. 15(4), 1404–1413 (2007)
Du, W., Zhan, Z.: A practical approach to solve secure multi-party computation problems. In: 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: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644. ACM, New York (2002)
Goethals, B., Laur, S., Lipmaa, H., Mielikainen, T.: On private scalar product computation for privacy-preserving data mining. In: 7th Annual International Conference on Information Security and Cryptology, pp. 104–120 (2004)
Zhu, Y., Huang, L., Yang, W.: Relation of PPAtMP and scalar product protocol and their applications. In: IEEE symposium on Computers and Communications, pp. 184–189 (2010)
Zhu, Y., Huang, L., Yang, W., Li, D., Luo, Y., Dong, F.: Three new approaches to privacy-preserving add to multiply protocol and its application. In: Second International Workshop on Knowledge Discovery and Data Mining, pp. 554–558 (2009)
Amirbekyan, A., Estivill-Castro, V.: A new efficient privacy-preserving scalar product protocol. In: 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: 2010 43rd Hawaii International Conference on System Sciences, pp. 1–9. IEEE Computer Society (2010)
Dong, C., Chen, L.: A fast secure dot product protocol with application to privacy preserving association rule mining. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 606–617. Springer, Heidelberg (2014)
Zhu, Y., Takagi, T., Huang, L.: Efficient secure primitive for privacy preserving distributed computations. In: Hanaoka, G., Yamauchi, T. (eds.) IWSEC 2012. LNCS, vol. 7631, pp. 233–243. Springer, Heidelberg (2012)
Zhu, Y., Takagi, T.: Efficient scalar product protocol and its privacy-preserving application. Int. J. Electron. Secur. Digit. Forensics 7(1), 1–19 (2015)
Acknowledgement
This work is partly supported by the Fundamental Research Funds for the Central Universities (No. NZ2015108), the Natural Science Foundation of Jiangsu Province of China (No. BK20150760), the China Postdoctoral Science Foundation funded project (No. 2015M571752), and the Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1402033C). We want to thank Prof. Wei Yang for his helpful discussion with us.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhu, Y., Wang, Z., Hassan, B., Zhang, Y., Wang, J., Qian, C. (2016). Fast Secure Scalar Product Protocol with (almost) Optimal Efficiency. In: Guo, S., Liao, X., Liu, F., Zhu, Y. (eds) Collaborative Computing: Networking, Applications, and Worksharing. CollaborateCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-28910-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-28910-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28909-0
Online ISBN: 978-3-319-28910-6
eBook Packages: Computer ScienceComputer Science (R0)