A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user's location. Hence, it will pose a huge threat to user's privacy if the sensing locations are published without protection. As usual, the user's location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user's sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user's location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism.