PrSLoc: Sybil attack detection for localization with private observers using differential privacy

Abstract

During the localization process in wireless networks, risks such as Sybil attacks and nodes’ location privacy leakage can exist due to the open and shared nature of wireless networks. However, it is challenging to obtain accurate location for sensor nodes while preserving the privacy of observer nodes who assist in sensor node localization while in the presence of Sybil attacks. Therefore, in this paper, we propose a secure and private localization algorithm, PrSLoc, based on Differential Privacy and Approximate Point-In-Triangulation (APIT, a range-free positioning algorithm). Specifically, APIT is applied for sensor nodes’ localization based on the received signal strength (RSS) from observer nodes as APIT doesn’t require special hardware or additional devices to estimate node positions. Besides, Differential Privacy technique is utilized to protect the privacy of observer nodes’ location information (i.e., nodes’ identity, location, and transmission power). Additionally, we mitigate Sybil attacks using a novel lightweight Sybil detection approach introduced in this paper that is based on the twice difference of the perceived RSS. Simulation results demonstrate that PrSLoc can efficiently eliminate Sybil nodes while preserving the observers’ privacy during wireless localization processes. Furthermore, the computation overhead and storage cost of the proposed PrSLoc are competitive compared with existing work. Our code can be found here: https://github.com/learning-lemon/PrSLoc-Sybil-Attack-Detection-for-Localization-with-Private-Observers-using-Differential-Privacy.git.