User identity privacy protection method and system based on zero-knowledge proof

Abstract

A user identity privacy protection method based on zero-knowledge proof, including: S1. Identity key request: the data user sends identity key generation request information to the identity management center through an established secure channel; S2. Identity key generation: The identity management center uses the identity information of the data user; S3. Public parameter setting: setting the public parameters of the data user and the big data publishing platform; S4. Zero-knowledge proof protocol: executed between the data user and the big data publishing platform Interactive zero-knowledge proof protocol; S5. Shared key request: The data user initiates a shared key generation request message to the identity management center through a secure channel; S6. Shared key generation: The identity management center publishes a shared key for the data user and big data The platform generates a shared key; S7. Account and password generation: the big data release platform generates temporary accounts and corresponding passwords for data users, and establishes a secure channel.

Description

technical field [0001] The invention relates to the technical field of computer information security, in particular to a zero-knowledge proof-based user identity privacy protection method and system. Background technique [0002] Big data contains a large amount of original information. Through the analysis and use of big data, users can obtain knowledge laws and development trends about things more effectively and accurately. However, while big data brings convenience and benefits to users, it also brings harm to the leakage of user identity and privacy. [0003] A large number of facts show that when users use big data, their identity information and behavior status will be recorded, and their identity privacy is facing a great threat. Moreover, in the big data environment, the threat faced by users is not limited to the leakage of personal privacy, but also lies in the prediction of user status and behavior based on big data. Many researches and implementations often be...

AI Technical Summary

Problems solved by technology

But in fact, only through anonymity and generalization can not protect the user's identity privacy very well

This is due to the cumulative and associated nature of user record data, a single record may not expose the user's identity privacy, but if multiple user behavior records can be associated together, the user's identity privacy may be compromised. exposed

Images