A privacy protection method for user demand trajectory

Abstract

The present invention provides a privacy protection method for user demand track, assuming that the user has M demands: S={s 1 ,s 2 ,...,s M}, the user’s demand is represented as a discrete-time trajectory at time T={1,2,...}; event <s,t>Denoted as the demand S at time t; the replacement set O is consistent with the user’s real demand set S; the target event is S tar ;O pre Represents the subset of replacement things of the query thing before the current demand query; O cur A replacement transaction that represents the user's desired transaction at the current time, known to both the attacker and the user. The beneficial effects of the present invention are: it can effectively protect the user's query content track privacy in social networks, and add Laplace noise (differential privacy) to the confidence between required things, so that the user's track privacy is further protected ; By using the privacy protection method of the game, not only the user's demand privacy is protected, but the user's service quality can also be well guaranteed.

Description

technical field [0001] The invention relates to a privacy protection method, in particular to a user demand track privacy protection method. Background technique [0002] At present, the existing technologies basically protect the privacy of the user's location trajectory. There are three broad categories of location privacy protection technologies, K-anonymous generalization technology, noise technology and dynamic pseudonym method. In 2003, Gruteser et al. first applied the K-anonymity method to protect the real location of users. In a certain time and space, each user needs to be indistinguishable from other K-1 users, so the attacker cannot distinguish the target user and infer the user's location. Later, some researchers applied K-anonymity generalization to protect trajectory privacy. In 2009, Ghintta et al. proposed to construct an anonymous area based on the user's moving speed, but this method is vulnerable to attacks and cannot guarantee the user's location privacy...

AI Technical Summary

Problems solved by technology

In 2003, Gruteser et al. first applied the K-anonymity method to protect the real location of users. In a certain time and space, each user needs to be indistinguishable from other K-1 users, so the attacker cannot distinguish the target user and infer the user's location

[0003] However, there is currently no special demand trajectory privacy method to protect people's demand trajectory privacy issues, which makes people's demand privacy seriously leaked and has a serious impact on people's lives

Images