Sign-in data dynamic collection and release method and system based on B-DP differential privacy mechanism

Abstract

The invention provides a sign-in data dynamic collection and release method and system based on a B-DP differential privacy mechanism, and the method comprises an initialization process: initializing a disturbance probability matrix Q and sign-in data estimation distribution by a data collection end, and broadcasting the disturbance probability matrix Q and the sign-in data estimation distribution to a data providing end; in the disturbance process, enabling the data providing end to generate sign-in data, and disturbing the sign-in state by using the probability matrix Q; in the aggregation process, enabling the data acquisition end to collect sign-in data, counting the sign-in data once after a period of time, and obtaining disturbance estimation distribution of the current time slice; in the reconstruction process, enabling the data acquisition end to obtain the original estimation distribution of the sign-in data of the current time slice in an inversion estimation mode, and obtaining the to-be-published estimation distribution through correction; in the publishing process, enabling the data acquisition end to send the current to-be-published estimation distribution to the data use end; and in the updating process, enabling the data acquisition end to perform comparison and judgment, and if an updating starting condition is met, utilizing a B-DP mechanism to obtain a new disturbance probability matrix Q, and broadcasting the new disturbance probability matrix Q to the data providing end.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a method and system for dynamically collecting and publishing sign-in data based on a B-DP differential privacy mechanism. Background technique [0002] With the rapid development of the mobile Internet, the rapid advancement of positioning technology and the widespread popularity of mobile devices, location-based service (LBS, Location Based Service) applications, including social platforms such as Foursquare, Gowalla and Facebook, generate a large amount of check-in data every day . Check-in data generally includes information such as the user's visit time, location, check-in point POI (Points Of Interest) attributes, mood, and evaluation of its visit location, so the check-in data becomes a carrier of the user's life track and interest tendency. This "user" is the so-called check-in person, and through the check-in service, he can share the POIs he visits (such...

AI Technical Summary

Problems solved by technology

[0006] (1) The differential privacy protection method that prioritizes privacy is often unacceptable in practical applications;

[0007] (2) For some differential privacy protection methods that take into account usability, the privacy protection is often very weak, or even almost no privacy at all

However, this trade-off is also difficult to avoid weakening or leaking privacy

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