摘要
A secure scalar product protocol is a type of specific secure multi-party computation problem. Using this kind of protocol, two involved parties are able to jointly compute the scalar product of their private vectors：, but no party will reveal any information about his/her private vector to another one. The secure scalar product protocol is of great importance in many privacy-preserving applications such as privacy-preserving data mining, privacy-preserving cooperative statistical analysis, and privacy-preserving geometry computation. In this paper, we give an efficient and secure scalar product protocol in the presence of malicious adversaries based on two important tools： the proof of knowledge of a discrete logarithm and the verifiable encryption. The security of the new protocol is proved under the standard simulation-based definitions. Compared with the existing schemes, our scheme offers higher efficiency because of avoiding inefficient cut-and-choose proofs.
A secure scalar product protocol is a type of specific secure multi-party computation problem. Using this kind of protocol, two involved parties are able to jointly compute the scalar product of their private vectors：, but no party will reveal any information about his/her private vector to another one. The secure scalar product protocol is of great importance in many privacy-preserving applications such as privacy-preserving data mining, privacy-preserving cooperative statistical analysis, and privacy-preserving geometry computation. In this paper, we give an efficient and secure scalar product protocol in the presence of malicious adversaries based on two important tools： the proof of knowledge of a discrete logarithm and the verifiable encryption. The security of the new protocol is proved under the standard simulation-based definitions. Compared with the existing schemes, our scheme offers higher efficiency because of avoiding inefficient cut-and-choose proofs.
基金
This work was supported by the National Natural Science Foundation of China under Grant Nos. 60973134, 61173164, 61003232, and the Natural Science Foundation of Guangdong Province of China under Grant No. 10351806001000000.
