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2021-01-28
Li, Y., Chen, J., Li, Q., Liu, A..  2020.  Differential Privacy Algorithm Based on Personalized Anonymity. 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). :260—267.

The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms.

2020-08-13
Yu, Lili, Su, Xiaoguang, Zhang, Lei.  2019.  Collaboration-Based Location Privacy Protection Method. 2019 IEEE 2nd International Conference on Electronics Technology (ICET). :639—643.
In the privacy protection method based on user collaboration, all participants and collaborators must share the maximum anonymity value set in the anonymous group. No user can get better quality of service by reducing the anonymity requirement. In this paper, a privacy protection algorithm random-QBE, which divides query information into blocks and exchanges randomly, is proposed. Through this method, personalized anonymity, query diversity and location anonymity in user cooperative privacy protection can be realized. And through multi-hop communication between collaborative users, this method can also satisfy the randomness of anonymous location, so that the location of the applicant is no longer located in the center of the anonymous group, which further increases the ability of privacy protection. Experiments show that the algorithm can complete the processing in a relatively short time and is suitable for deployment in real environment to protect user's location privacy.