| Title | Localized Differential Location Privacy Protection Scheme in Mobile Environment |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Kai, Liu, Jingjing, Wang, Yanjing, Hu |
| Conference Name | 2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI) |
| Date Published | jul |
| Keywords | Big Data, composability, Data models, Differential privacy, Human Behavior, information processing, k-anonymity, location privacy protection, Markov model, Markov processes, privacy, pubcrawl, resilience, Resiliency, Scalability, simulation |
| Abstract | When users request location services, they are easy to expose their privacy information, and the scheme of using a third-party server for location privacy protection has high requirements for the credibility of the server. To solve these problems, a localized differential privacy protection scheme in mobile environment is proposed, which uses Markov chain model to generate probability transition matrix, and adds Laplace noise to construct a location confusion function that meets differential privacy, Conduct location confusion on the client, construct and upload anonymous areas. Through the analysis of simulation experiments, the scheme can solve the problem of untrusted third-party server, and has high efficiency while ensuring the high availability of the generated anonymous area. |
| DOI | 10.1109/BDAI56143.2022.9862753 |
| Citation Key | kai_localized_2022 |
Localized Differential Location Privacy Protection Scheme in Mobile Environment