A K-anonymity Optimization Algorithm Under Attack Model

The current k-anonymity algorithm still suffers from problems such as location information leakage and poor quality of service due to improper selection of k. To tackle the above drawbacks, we propose a personalized k-anonymity optimization algorithm under attack model. Achieving optimal quality of service and personalized privacy protection in a context where attackers launch the strongest attacks based on a priori knowledge. Quantifying location privacy and quality of service based on location entropy and attack models. On this basis, the optimal k is obtained through the game to achieve privacy and service quality optimization. Security analysis and simulation experiments demonstrate the privacy and effectiveness of the algorithm. The experimental results show the specific k for different scenarios and different anonymity mechanisms.