An enhanced privacy-preserving indoor localization scheme based on WiFi fingerprint recognition

With the proliferation of mobile smart devices, location data has become a critical asset in various applications. WiFi-based fingerprint positioning technology is one of the commonly used indoor positioning methods. However, the significant concern of privacy leakage has emerged as a crucial obstacle to its advancement. To address this issue, an enhanced privacy-preserving WiFi fingerprint localization scheme based on fingerprint recognition is proposed. The scheme initially employs the dummy fingerprint generation algorithm to create dummy location fingerprints that closely resemble the actual distribution, thereby obfuscating the user’s true localization requests. The server then utilizes the Paillier homomorphic encryption algorithm for matrix multiplication selection to return encrypted query results corresponding to the real fingerprints, ensuring that user privacy data remains secure throughout the process. Furthermore, an enhanced dummy fingerprinting algorithm is proposed, aiming to optimize the movement entropy by leveraging location association information, hence improving location anonymity. Theoretical analysis and experimental results demonstrate the safety, effectiveness and practicality of the proposed scheme.