True value discovery method satisfying localized differential privacy and electronic equipment

Abstract

The invention provides a truth value discovery method satisfying localized differential privacy and an electronic device, at each time point, a client uses a single sample t test method in significance test to determine whether original data change between adjacent time points is significant so as to determine whether to update voting information of a truth value and upload the voting information to a server after disturbance; the server summarizes the voting information of all the clients to determine a task that truth value discovery needs to be carried out again at the current time point; and the client locally disturbs data of a corresponding task and uploads the data to the server, and the server carries out dynamic truth value discovery considering the weight of the client and the time smoothness of the task truth value so as to update the task truth value of the current time point. In this way, the privacy of the original data of the client at each time point can be protected, and meanwhile high truth value discovery accuracy can be guaranteed.

Description

technical field [0001] The present application relates to the technical field of truth discovery, and in particular to a truth discovery method and electronic equipment that satisfy localized differential privacy. Background technique [0002] Privacy-preserving dynamic truth-value discovery technology provides a feasible solution to solve the problem of leakage of users' personal privacy caused by dynamic truth-value discovery. Localized differential privacy technology (LDP, Local Differential Privacy) does not need to use a trusted third-party server. By allowing users to perturb data locally before uploading data, only users themselves can access the original data, so it fundamentally prevents users from Personal privacy leakage, applied to the dynamic truth discovery task can protect the user's personal privacy. [0003] When the number of time points in the time domain of the truth discovery task is large, independent data perturbation at each time point will lead to l...

AI Technical Summary

Problems solved by technology

[0003] When the number of time points in the time domain of the truth discovery task is large, independent data perturbation at each time point will lead to low accuracy of truth discovery; existing truth discovery methods only use current time point data to estimate client weights , sometimes it is necessary to assume that the client’s original data satisfies a certain distribution before dynamic truth discovery can be performed; in terms of privacy-preserving parameter allocation, the existing truth-value discovery methods need to use a one-bit protocol to solve the allocation problem of privacy-preserving parameters, resulting in true Value finding with low accuracy

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