Differential privacy protection method and system for track time mode

Abstract

The invention relates to a differential privacy protection method and system for a track time mode, and belongs to the field of data mining. The method comprises the following steps: firstly, based onan anonymization thought, carrying out coarse-grained disturbance on track time data by using kanonymity, and anonymously hiding data in an original single time period into a whole day; then, using aLaplace mechanism for carrying out fine-grained disturbance on a timestamp of a track; and finally, limiting the noise disturbance error within a fixed range based on a truncation Laplace mechanism,thereby improving the precision of the published data. The problem of individual privacy leakage caused by periodicity of the track time mode is solved.

Description

technical field [0001] The invention belongs to the field of data mining, and relates to a differential privacy protection method and system for trajectory time patterns. Background technique [0002] With the rapid development of GPS and WIFI, a large amount of spatio-temporal data is generated. Trajectories are spatio-temporal data based on time sampling, including attributes such as sampling point locations, sampling time, and sampling speed. Since spatio-temporal trajectory data has periodicity in time patterns, clustering and correlation analysis can obtain a large amount of personal privacy information. For example, by observing the fixed location of the user during the morning rush hour from Monday to Friday, it can be deduced that the location has a high probability of being the user's home address. In recent years, due to the frequent occurrence of privacy leakage events caused by the trajectory time pattern, it has posed a great threat to the user's property secur...

AI Technical Summary

Problems solved by technology

[0006] (3) The k-anonymous protection model cannot provide an effective and strict method to prove its security. When the k value changes, its security cannot be quantitatively analyzed

[0008] Although there have been many research results applying differential privacy to trajectory data release, most of them are based on trajectory shape mode and OD (Origin-Destination) flow mode, and there are few researches on privacy protection of trajectory time mode, especially in combination with user There are few studies on the activity area to infer its residence and work points

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