A privacy-preserving protocol for indoor Wi-Fi localization

Thumbnail Image
Eshun, Samuel N.
Palmieri, Paolo
Journal Title
Journal ISSN
Volume Title
Association for Computing Machinery (ACM)
Published Version
Research Projects
Organizational Units
Journal Issue
Location-aware applications have witnessed massive worldwide growth in recent years due to the introduction and advancement of smartphones. Most of these applications rely on the Global Positioning System (GPS) which is not available in indoor environments. As a result, Wi-Fi fingerprinting is becoming increasingly popular as an alternative as it allows localizing users in indoor environments, has lower power consumption, and is also more economical as it does not require a dedicated sensor other than a Wi-Fi card. The technique allows a service provider (SP) to construct a Wi-Fi database (called radio map) that can be used as a reference point to localize a user. However, this process does not preserve the user privacy, as the location can only be computed interactively with the SP. The service provider may also reveal sensitive information on the indoor space (e.g. the building map) to the user. Thus, we need an indoor localization protocol that addresses the privacy of both parties. In this paper, we present a privacy-preserving cryptographic protocol for indoor Wi-Fi localization, that prevents the SP from learning the exact location of the user outside of certain pre-defined sensitive areas, while keeping the SP's database secure. Thus, both parties cannot learn anything about each other's input beyond the implicit output revealed.
Location privacy , Cryptographic protocols , Bloom filter
Eshun, S. N. and Palmieri, P. (2019) 'A privacy-preserving protocol for indoor Wi-Fi localization', Proceedings of the 16th ACM International Conference on Computing Frontiers, Alghero, Italy, 30 April - 2 May, pp. 380-385. doi: 10.1145/3310273.3323400
© 2019, the Authors. Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.