This paper introduces the current research status of the multi-party computation protocols,briefly showsthe current four types of multi-party computation protocols and summarizes the research direction.
In the field of sequencing of secret number,an important problem is how to establish an efficient and secure protocol for sorting the secret number.As a powerful tool in solving privacy sequencing problems,secure mult...In the field of sequencing of secret number,an important problem is how to establish an efficient and secure protocol for sorting the secret number.As a powerful tool in solving privacy sequencing problems,secure multipart computation is more and more popular in anonymous voting and online auction.In the present study,related secure computation protocol for sequencing problem is not many by far.In order to improve the efficiency and safety,we propose a security sequencing protocol based on homomorphic encryption.We also give analysis of correctness and security to highlight its feasibility.展开更多
Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed s...Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data perturbation techniques are comparatively efficient but are mainly used in centralized privacy-preserving data mining (PPDM). In this paper, we propose a light-weight anonymous data perturbation method for efficient privacy preserving in distributed data mining. We first define the privacy constraints for data perturbation based PPDM in a semi-honest distributed environment. Two protocols are proposed to address these constraints and protect data statistics and the randomization process against collusion attacks: the adaptive privacy-preserving summary protocol and the anonymous exchange protocol. Finally, a distributed data perturbation framework based on these protocols is proposed to realize distributed PPDM. Experiment results show that our approach achieves a high security level and is very efficient in a large scale distributed environment.展开更多
文摘This paper introduces the current research status of the multi-party computation protocols,briefly showsthe current four types of multi-party computation protocols and summarizes the research direction.
基金supported by the National Natural Science Foundation of China under Grant No.51307004
文摘In the field of sequencing of secret number,an important problem is how to establish an efficient and secure protocol for sorting the secret number.As a powerful tool in solving privacy sequencing problems,secure multipart computation is more and more popular in anonymous voting and online auction.In the present study,related secure computation protocol for sequencing problem is not many by far.In order to improve the efficiency and safety,we propose a security sequencing protocol based on homomorphic encryption.We also give analysis of correctness and security to highlight its feasibility.
基金Project supported by the National Natural Science Foundation of China (Nos. 60772098 and 60672068)the New Century Excel-lent Talents in University of China (No. NCET-06-0393)
文摘Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data perturbation techniques are comparatively efficient but are mainly used in centralized privacy-preserving data mining (PPDM). In this paper, we propose a light-weight anonymous data perturbation method for efficient privacy preserving in distributed data mining. We first define the privacy constraints for data perturbation based PPDM in a semi-honest distributed environment. Two protocols are proposed to address these constraints and protect data statistics and the randomization process against collusion attacks: the adaptive privacy-preserving summary protocol and the anonymous exchange protocol. Finally, a distributed data perturbation framework based on these protocols is proposed to realize distributed PPDM. Experiment results show that our approach achieves a high security level and is very efficient in a large scale distributed environment.
