Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization

Abstract

Location privacy poses a critical challenge as the use of mobile devices and location-based services becomes more and more widespread. Proximity-detection data can reveal sensitive information about individuals, making it essential to preserve their location data. One way to achieve privacy protection is by adding noise to ground-truth data, which can introduce uncertainty while still allowing moderate utility for proximity-detection services and Received Signal Strength (RSS)-based localization. However, it is important to carefully adjust the amount of noise added in order to balance the privacy and accuracy concerns. This paper expands our previous work on evaluating location privacy bounds based on measurement error and intentionally added noise. Our model builds upon existing work in differential privacy and introduces other techniques to estimate privacy bounds specific to proximity data. By using real-world measurement data, we measure the privacy-accuracy trade-off and suggest cases where additional noise could be added. Our framework can be utilized to inform privacy-preserving location-based applications and guide the selection of appropriate noise levels in order to achieve the desired privacy-accuracy balance.