论文信息 - Privacy Preserving k Secure Sum Protocol

Privacy Preserving k Secure Sum Protocol

Secure Multiparty Computation (SMC) allows parties to know the result of cooperative computation while preserving privacy of individual data. Secure sum computation is an important application of SMC. In our proposed protocols parties are allowed to compute the sum while keeping their individual data secret with increased computation complexity for hacking individual data. In this paper the data of individual party is broken into a fixed number of segments. For increasing the complexity we have used the randomization technique with segmentation

[1] Chris Clifton,et al. Tools for privacy preserving distributed data mining , 2002, SKDD.

[2] M. Atallah,et al. Secure Multi-Problem Computation Problems and Their Applications : A Review and Open Problems , 2001 .

[3] Silvio Micali,et al. How to play ANY mental game , 1987, STOC.

[4] Ramakrishnan Srikant,et al. Privacy-preserving data mining , 2000, SIGMOD '00.

[5] Niv Gilboa,et al. Computationally private information retrieval (extended abstract) , 1997, STOC '97.

[6] Durgesh Kumar Mishra,et al. A Secure Multi-Party Computation Protocol for Malicious Computation Prevention for preserving privacy during Data Mining , 2009, ArXiv.

[7] Yehuda Lindell,et al. Secure Multiparty Computation for Privacy-Preserving Data Mining , 2009, IACR Cryptol. ePrint Arch..

[8] Wenliang Du,et al. Protocols for Secure Remote Database Access with Approximate Matching , 2001, E-Commerce Security and Privacy.

[9] Joachim Biskup,et al. On Pseudonymization of Audit Data for Intrusion Detection , 2000, Workshop on Design Issues in Anonymity and Unobservability.

[10] Wenliang Du,et al. Privacy-preserving cooperative scientific computations , 2001, Proceedings. 14th IEEE Computer Security Foundations Workshop, 2001..

[11] Wenliang Du,et al. Secure Multi-party Computational Geometry , 2001, WADS.