Homomorphic privacy set intersection method based on order-preserving function

Abstract

The invention belongs to the technical field of privacy computing, and relates to a homomorphic privacy set intersection method based on an order-preserving function. The homomorphic privacy set intersection method is characterized in that: sensitive data is processed by adopting an order-preserving function before the sensitive data is subjected to disk storage, and then the data is stored in a hard disk, so that the sensitive data is subjected to encryption protection when the sensitive data is subjected to disk storage, and the size characteristics of the data is reserved. During privacy set intersection, after data of a client side are processed through an order-preserving function, homomorphic encryption is carried out on the data of the client side, after homomorphic encryption is completed, intersection operation of the privacy sets is carried out on the server side through the characteristics of the homomorphic calculation technology under the condition that the data of the client side and the data of the server side are not plaintext, the solution mode is particularly suitable for the condition that data sets of two parties are unbalanced, the data operation communication traffic is reduced, the operation efficiency is improved, the data participating in the operation are operated in a ciphertext state, and the security of the privacy data is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of privacy computing, and in particular relates to a homomorphic privacy set intersection method based on an order-preserving function. Background technique [0002] With the rapid development of communication technology and network technology and the wide application of mobile computing, cloud computing, and distributed computing, virtual networks are more closely connected with people's lives, and various applications of Internet big data have penetrated into people's social networking, shopping, travel etc. These applications allow people to enjoy more convenient services, but at the same time a large amount of valuable customer information, personal privacy records, and business operation data are constantly being mined, and people's privacy is increasingly threatened. Therefore, privacy in the era of big data Protection issues have become the focus of attention in academia and industry. [0003] From ...

AI Technical Summary

Problems solved by technology

[0006] However, in practical applications, when the number of private sets involved in the calculation is unbalanced (unbalancedsets), the communication cost is very high, especially when the number of data sets of one party is much larger than that of the other party. Operational efficiency is very low

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