Non-center dot product computing method achieving privacy protection

Abstract

The invention provides a non-center dot product computing method achieving privacy protection. The non-center dot product computing method achieving privacy protection includes the following steps that 1, a first participant and a second participant are connected with a trusted third party, so that corresponding privacy vector certificates of the first participant and the second participant are obtained, wherein the certificates comprise privacy vector ciphertext; 2, the first participant sends the certificate of the first participant to the second participant; 3, the second participant carries out ciphertext homomorphic dot product computation on the basis of the privacy vector ciphertext in the certificate of the first participant, and sends the dot product ciphertext and the certificate of the second participant to the first participant; 4, the first participant decodes the dot product ciphertext to obtain the privacy vector dot product of the first participant and the privacy vector dot product of the second participant. Safety multi-party dot product computation is achieved without participation of a center server, input and output verifiability is ensured for users, and the requirements of the users for personal data privacy protection and the reliability of dot product computing results in a not credible environment are met.

Description

technical field [0001] The invention relates to the field of secure multi-party calculations, in particular to a privacy-protecting centerless dot product calculation method. Background technique [0002] Secure multi-party computation that protects privacy is widely used in various fields in real life. Secure multi-party computing is to use multi-party data to perform calculations without exposing the data privacy of the computing participants, and only expose the computing results to the computing participants. For example, user similarity calculation in social networks, privacy-protected voting and auctions, secure data collection and sorting and data outsourcing calculations in sensor networks, etc. The easiest way to solve this problem is to use a trusted central server. Computing participants will hand over their private data to the trusted center, which will perform calculations and then return the calculation results to the participants. However, in many cases, not...

AI Technical Summary

Problems solved by technology

At present, these applications generally use trusted central servers, and a small number of calculations that use a non-central method also ignore the verification of user input and output, resulting in a large privacy loophole in these methods

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