Continuous Release of Location Data Based on Differential Privacy

Nowadays, location data sharing has become an important way for people to share their lives and exchange for location-based services. The release of location data maximizes the value of the data, but also leads to personal privacy leakage. Existing research work on differential privacy-based data publish scheme use a grouping mechanism to improve the utility of release data. However, it requires more pre-defined parameters, besides, its data protection process does not adequately consider data variation characteristics. In this paper, we propose a location data continuous release privacy protection framework, called LDCR, which provides $w$ -event privacy protection for the release of location aggregated data. We define data change rate, which captures the data trends utilizing the change in the tilt angle of the data slope at adjacent moments. Meanwhile, we design a grouping mechanism based on data change rate to reduce the number of pre-defined parameters, and a privacy budget allocation mechanism that adapt to data changes to improve the rationality of privacy budget application. Experimental results show that our proposed mechanism can provide privacy protection for the continuous release of location data while ensuring data utility.