Distributed Range-Only Localization That Preserves Sensor and Navigator Privacies

• 成果类型: Article
• 署名作者: Ristic, Marko; Noack, Benjamin; Hanebeck, Uwe D.
• 署名单位: Otto von Guericke University; Helmholtz Association; Karlsruhe Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3263740
• 发表日期: 2023
• 页码: 7151-7163
• 关键词: Data privacy; Extended Kalman filter (EKF); Sensor fusion; State estimation

摘要

Distributed state estimation and localization methods have become increasingly popular with the rise of ubiquitous computing, and have led naturally to an increased concern regarding data and estimation privacy. Traditional distributed sensor navigation methods typically involve the leakage of sensor or navigator information by communicating measurements or estimates and thus do not preserve participants' privacy. The existing approaches that do provide such guarantees fail to address sensor and navigator privacy in the common application of model-based range-only localization, consequently forfeiting broad applicability. In this work, we define a notion of privacy-preserving linear combination aggregation and use it to derive a modified extended Kalman filter using range measurements such that navigator location, sensors' locations, and sensors' measurements are kept private during navigation. Additionally, a formal cryptographic backing is presented to guarantee our method's privacy as well as an implementation to evaluate its performance. The novel, provably secure, range-based localization method has applications in a variety of environments where sensors may not be trusted or estimates are considered sensitive, such as autonomous vehicle localization or air traffic navigation.