Experimental data set.

It is a new attack model to mine user’s activity rule from user’s massive data. In order to solve the privacy leakage problem caused by user tendency in current privacy preserving methods, an extended differential privacy preserving method based on user’s tendency is proposed in the paper. By constructing a Markov chain, and using the Markov decision process, it equivalently expresses user’s tendency as measurable state transition probability, which can transform qualitative descriptions of user’s tendency into a quantitative representation to achieve an accurate measurement of the user tendency. An extended (P,ε)-differential privacy protection method is proposed in the work, by introducing a privacy model parameter R, it combines the quantified user’s propensity probability with a differential privacy budget parameter, and it can dynamically add different noise amounts according to the user’s tendency, so as to achieve the purpose of protecting the user’s propensity privacy information and improve data availability. Finally, the feasibility and effectiveness of the proposed method was verified by experiments.