| Title | Trajectory Protection Scheme Based on Fog Computing and K-anonymity in IoT |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Zhou, Kexin, Wang, Jian |
| Conference Name | 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) |
| Keywords | anonymity, cloud computing, cloud computing technology, composability, Computational modeling, data protection, edge computing, Entropy, Fog Computing, Human Behavior, Internet of Things, IoT, k-anonymity, k-anonymity-based dummy generation algorithms, location based services, location-based services, maximum entropy, maximum entropy methods, Metrics, mobile computing, offline trajectory data protection, offline trajectory protection, online trajectory protection, privacy, probability, pubcrawl, real-time trajectory privacy protection, resilience, Resiliency, security, security analysis, security of data, Servers, time-dependent query probability, Trajectory, trajectory protection, trajectory protection scheme, trajectory publication, transition probability |
| Abstract | With the development of cloud computing technology in the Internet of Things (IoT), the trajectory privacy in location-based services (LBSs) has attracted much attention. Most of the existing work adopts point-to-point and centralized models, which will bring a heavy burden to the user and cause performance bottlenecks. Moreover, previous schemes did not consider both online and offline trajectory protection and ignored some hidden background information. Therefore, in this paper, we design a trajectory protection scheme based on fog computing and k-anonymity for real-time trajectory privacy protection in continuous queries and offline trajectory data protection in trajectory publication. Fog computing provides the user with local storage and mobility to ensure physical control, and k-anonymity constructs the cloaking region for each snapshot in terms of time-dependent query probability and transition probability. In this way, two k-anonymity-based dummy generation algorithms are proposed, which achieve the maximum entropy of online and offline trajectory protection. Security analysis and simulation results indicate that our scheme can realize trajectory protection effectively and efficiently. |
| DOI | 10.23919/APNOMS.2019.8893014 |
| Citation Key | zhou_trajectory_2019 |
Trajectory Protection Scheme Based on Fog Computing and K-anonymity in IoT