Secure Multi-party Computation Using Virtual Parties for Computation on Encrypted Data
In this paper, we propose a new Virtual Party Protocol (VPP) protocol for Secure Multi-Party Computation (SMC). There are many computations and surveys which involve confidential data from many parties or organizations. As the concerned data is property of the organization or the party, preservation and security of this data is of prime importance for such type of computations. Although the computation requires data from all the parties, but none of the associated parties would want to reveal their data to the other parties. We have proposed a new protocol to perform computation on encrypted data. The data is encrypted in a manner that it does not affect the result of the computation. It uses modifier tokens which are distributed among virtual parties, and finally used in the computation. The computation function uses the acquired data and modifier tokens to compute right result from the encrypted data. Thus without revealing the data, right result can be computed and privacy of the parties is maintained. We have given a probabilistic security analysis and have also shown how we can achieve zero hacking security with proper configuration.
KeywordsSecure Multi-party Computation (SMC) Information Security Privacy Protocol
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- 1.Yao, A.C.: Protocols for secure computations. In: Proc. of 23rd Annual Symposium Foundations of Computer Science, pp. 160–164Google Scholar
- 2.Atallah, M., Bykova, M., Li, J., Frikken, K., Topkara, M.: Private collaborative forecasting and benchmarking. In: Proc. of the 2004 ACM workshop on Privacy in the Electronic Society (2004)Google Scholar
- 3.Atallah, M., Bykova, M., Li, J., Frikken, K., Topkara, M.: Private collaborative forecasting and benchmarking. In: Proc. of the 2004 ACM workshop on Privacy in the electronic society, pp. 103–114 (2004)Google Scholar
- 4.Du, W., Zhan, Z.: A practical approach to solve secure multi-party computation problems. In: Proc. of the New Security Paradigms Workshop (2002)Google Scholar
- 5.Null, L.M., Wong, J.: A unified approach for multilevel database security based on inference engines. Transaction of ACM 21(1) (February 1989)Google Scholar
- 6.Wenliang Du; Atallah, M.J.: Privacy-preserving cooperative scientific computations. In: Proc. 14th IEEE Computer Security Foundations Workshop, June 11-13, pp. 273–282 (2001)Google Scholar
- 7.Canetti, R., Feige, U., Goldreich, O., Naor, M.: Adaptively secure multi-party computation. In: Proc. The 28th annual ACM symposium on Theory of computingGoogle Scholar
- 8.Atallah, M.J.: Secure and Private Sequence Comparisons. In: Proc. The 2003 ACM workshop on Privacy in the electronic society (2003)Google Scholar
- 9.Atallah, M.J., Elmongui, H.G., Deshpande, V., Schwarz, L.B.: Secure supply-chain protocols. In: Proc. IEEE International Conference, E-Commerce (2003)Google Scholar
- 10.Maurer, U.: The role of cryptography in database security. In: Proc. The 2004 ACM SIGMOD international conference on Management of data (2004)Google Scholar
- 11.Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proc. The ACM SIGMOD Conference on Management of Data (2000)Google Scholar
- 12.Mishra, D.K., Chandwani, M.: Anonymity enabled secure multi-party computation for Indian BPO. In: Proceeding of the IEEE Tencon 2007: International conference on Intelligent Information Communication Technologies for Better Human Life, Taipei, Taiwan, October 29- November 02, pp. 52–56 (2007)Google Scholar