Location Privacy Protection in Mobile Social Networks Based on l-Diversity
Linxia Gong, Feng Guo, Quanli Miao · 2019
In recent years, location-based service has been widely used in social networks. While more and more users enjoy the convenience of location-based services, the user's location privacy is also facing various threats of privacy disclosure. Aiming at the problem of location privacy disclosure in mobile social network applications, this paper proposed a location privacy protection method for multi-sensitive attributes based on l-diversity privacy protection model, and protected the user's location information in client side and server respectively. On the client side, the decomposition algorithm of minimum distance grouping is used to lighten the location data, which makes the processed data satisfy the l1-diversity principle and upload the data to the server in the form of QIT1(Quasi-Identifier attribute Table) and ST1(Sensitive attribute Table) to achieve the initial protection of the user's location data. On the server side, the minimum selectivity priority strategy was adopted to form the l2-diversity group satisfying the multi-sensitive attributes, and the data was uploaded in the form of QIT2and ST2to further protect the user location data (where l12). The experimental results show that this method not only can effectively protect location privacy data, but also has high data availability.