Biblio

With the construction and implementation of the government information resources sharing mechanism, the protection of citizens' privacy has become a vital issue for government departments and the public. This paper discusses the risk of citizens' privacy disclosure related to data sharing among government departments, and analyzes the current major privacy protection models for data sharing. Aiming at the issues of low efficiency and low reliability in existing e-government applications, a statistical data sharing framework among governmental departments based on local differential privacy and blockchain is established, and its applicability and advantages are illustrated through example analysis. The characteristics of the private blockchain enhance the security, credibility and responsiveness of information sharing between departments. Local differential privacy provides better usability and security for sharing statistics. It not only keeps statistics available, but also protects the privacy of citizens.

With the development of location technology, location-based services greatly facilitate people's life . However, due to the location information contains a large amount of user sensitive informations, the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data . To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set. Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.

Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.