PLDP: Personalized LDP for Collecting and Analyzing Multidimensional Data

The popularity of crowdsourcing applications accelerates the development of enterprises, and the privacy leakage has become the focus of public attention. The existing local differential privacy (LDP) mechanism mainly focuses on the utility optimization of a single privacy level, which will cause some users to refuse to share data due to insufficient privacy protection level, while some users get too much privacy protection. In order to meet different privacy protection needs of users, this paper proposes a personalized local differential privacy (PLDP) mechanism for collecting and analyzing multi-dimensional mixed data, which provides multiple privacy protection levels for users. Specifically, this paper proposes a personalized user data perturbation framework, which implements personalized mean estimation algorithm and frequency estimation algorithm for numerical data and classified data respectively, and proves the confidentiality and effectiveness of the algorithm through theoretical analysis. In addition, a personalized sampling scheme is proposed, which preprocesses the attribute tags according to preferences of the server, and biases the data dimensions according to their collection preferences. Experiments on two real datasets show that, compared with traditional LDP mechanism, the proposed mechanism not only guarantees the privacy of user data, but also reduces the statistical error of collecting numerical data and classified data, so it provides a better balance between privacy protection and data availability.