The Research And Implementation Of Personalized K-anonymous Location Privacy Protection Technology Based On The Internet Of Things

As the demands of LBS application increase, the limitation of traditional protection model for the location privacy has emerged. A more secure, faster and more intelligent technology is urgently needed. There are a great number of LBS users and the users require high-quality services, which brings a lot of challenges to the design of the privacy protection program.Based on the analysis of the location privacy protection model architecture and the mature K-anonymity technology, this thesis proposes a set of personalized K-anonymous location privacy protection schemes which meet the requirements of humanization, security and quick response. The scheme consists of two anonymous mods: a traditional anonymous mode and a fake anonymous mode. In the traditional anonymous mode, in order to improve the server response rate, an Anonymous Group Memory Algorithm is designed. The algorithm uses anonymous group multiplexing based on certain conditions in order to accelerate the speed of anonymization. Simulation experiment of the algorithm was compared with the traditional algorithm and the performance of the algorithm in high security level and low security level is analyzed. The results show that the anonymous group memory algorithm has significant speed advantage in high security level over traditional algorithm. In the fake anonymous mode, in order to improve the success rate of the server response, and to ensure the user's location privacy security, this thesis proposes a Real User Path Simulation Algorithm to simulate the real user activities to protect the real user location privacy. Simulation results show that the algorithm satisfies the users'security requirements.