Server-side Fingerprint-Based Indoor Localization Using Encrypted Sorting
This work addresses privacy issues in indoor localization for users in environments like university buildings, but it is incremental as it builds on existing homomorphic cryptographic methods.
The paper tackled indoor localization using Wi-Fi fingerprints while addressing privacy concerns by employing the DGK homomorphic algorithm for encrypted sorting, shifting most computations to the server side. Results from testing in a dormitory showed improved power savings on the client side compared to existing systems.
GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on a fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a client's daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy into the existing fingerprint-based approaches, they are limited to the characteristics of the homomorphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.