Robust Location Tag Generation from Noisy Location Data for Security Applications

Location-based security service provides authorization of persons or facilities based on their distinctive location information. It applies the field of position navigation and time (PNT) to the provision of security. Location parameters from radio navigation signals are mapped into a location verification tag or geotag to block or allow certain actions or access. Loran and Wi-Fi are chosen as case studies because of their good properties that are beneficial to location-based security services. The achievable performance and the security of the system are determined by the quantity and quality of location features. By quantity, we mean the total number of different (independent) location dependent measurements available. By quality, we mean that amount of unique location dependent information and its consistency provided by each parameter that can be used to generate a robust location tag. It is desirable to have the parameters be relatively insensitivity to temporal changes which weaken the uniqueness of the information. As a result, repeatability and repeatable accuracy are the fundamental requirement for any location-based security service. In this paper we propose fuzzy extractor scheme to generate strong location tag from noisy location data. A fuzzy extractor is an algorithm that can reliably extract desired information from input, and is error tolerant in the sense that this information will be the same even if the input changes. We develop different constructions of fuzzy extractor for various parameter distance measures: Euclidean and Hamming metrics. A combination use of the constructions will produce a more robust location tag. In addition, we mathematically analyze the entropy loss of the fuzzy extractor and study how to choose optimal design parameters under the tradeoff.