| Title | IoT Trajectory Data Privacy Protection Based on Enhanced Mix-zone |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lan, Jian, Gou, Shuai, Gu, Jiayi, Li, Gang, Li, Qin |
| Conference Name | 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) |
| Date Published | oct |
| Keywords | attack method, computer network security, Control Theory and Privacy, cyber physical systems, Cyber-physical systems, data privacy, data sets, enhanced Mix-zone, graph theory, Human Behavior, Information security, Internet of Things, IoT trajectory data privacy protection, migration probability, Mix-zone, privacy, privacy protection, probability, pubcrawl, Resiliency, Scalability, traditional Mix-zone, user flow-based algorithm, weighted undirected graph |
| Abstract | Trajectory data in the Internet of Things contains many behavioral information of users, and the method of Mix-zone can be used to separate the association among the user's movement trajectories. In this paper, the weighted undirected graph is used to establish a mathematical model for the Mix-zone, and a user flow-based algorithm is proposed to estimate the probability of migration between nodes in the graph. In response to the attack method basing on the migration probability, the traditional Mix-zone is improved. Finally, an algorithms for adaptively building enhanced Mix-zone is proposed and the simulation using real data sets shows the superiority of the algorithm. |
| DOI | 10.1109/IMCEC46724.2019.8983924 |
| Citation Key | lan_iot_2019 |
IoT Trajectory Data Privacy Protection Based on Enhanced Mix-zone