Visible to the public Biblio

Filters: Author is Zhang, Zhixiang  [Clear All Filters]
2022-04-26
Feng, Tianyi, Zhang, Zhixiang, Wong, Wai-Choong, Sun, Sumei, Sikdar, Biplab.  2021.  A Privacy-Preserving Pedestrian Dead Reckoning Framework Based on Differential Privacy. 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). :1487–1492.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

2020-06-22
Feng, Tianyi, Wong, Wai-Choong, Sun, Sumei, Zhao, Yonghao, Zhang, Zhixiang.  2019.  Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy. 2019 16th Workshop on Positioning, Navigation and Communications (WPNC). :1–6.
With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.