Privacy-preserving Indoor Localization Research Articles

Privacy-preserving indoor localization based on inner product encryption in a cloud environment

How to utilize the resource-rich cloud to provide scalable indoor localization service to solve the problem of limited user equipment resources while preventing privacy leakage has become a hot topic. A novel privacy-preserving indoor localization scheme based on Inner Product Encryption in a cloud environment is proposed to address this issue. By using the computability of Inner Product Encryption on ciphertext without exposing plaintext information, an honest-but-curious cloud server estimates the users location without obtaining any private information. The least-squares estimation algorithm of the Time Difference location system is decomposed into the basic form of the inner product of two sets of vectors. One is the anchor information encrypted with Inner Product Encryption during the offline phase, the other is the users distance difference information encrypted and sent to the cloud server during the online phase. To hide the users real location, the users real distance difference information and several decoy information are encrypted and included in a request to achieve k-Anonymity. The location estimation algorithm operates on the cloud to locate the user from the ciphertext. In addition, three optimization methods applied to cloud server location estimation algorithm were designed to further improve the performance. The experimental results and theoretical analysis show that the proposed scheme consumes less time, and has a lower communication overhead, while maintaining positioning accuracy. At the same time, the scheme protects the users locational and data privacy of the indoor positioning service provider when migrating the indoor positioning services to the cloud environment.

Practical Privacy-Preserving Indoor Localization Based on Secure Two-Party Computation

We present a privacy-preserving indoor localization scheme based on received signal strength measurements, e.g., from WiFi access points. Our scheme preserves the privacy of both the client's location and the service provider's database by using secure two-party computation instantiated with known cryptographic primitives, namely, Paillier encryption and garbled circuits. We describe a number of optimizations that reduce the computation and communication overheads of the scheme and provide theoretical evaluations of these overheads. We also demonstrate the feasibility of the scheme by developing a proof-of-concept implementation for Android smartphones and commodity servers. This implementation allows us to validate the practical performance of our scheme and to show that it is feasible for practical use in certain types of indoor localization applications.

DP3: A Differential Privacy-Based Privacy-Preserving Indoor Localization Mechanism

Wi-Fi fingerprint-based indoor localization is regarded as one of the most promising techniques for location-based services. However, it faces serious problem of privacy disclosure of both clients’ location data and provider’s fingerprint database. To address this issue, this letter proposes a differential privacy (DP)-based privacy-preserving indoor localization scheme, called DP3, which is composed of four phases: access point (AP) fuzzification and location retrieval in client side and DP-based finger clustering and finger permutation in server side. Specifically, in AP fuzzification , instead of providing the measured full finger (including AP sequence and the corresponding received signal strength), a to-be-localized (TBL) client only uploads the AP sequence to the server. Then, the localization server utilizes the DP-enabled clustering to build the fingerprints related to the AP sequence into $k$ clusters, permutes these reference points in each cluster with exponential mechanism to mask the real positions of these fingerprints, and sends the modified data set to the TBL client. At client side, location retrieval phase estimates the location of the client. Theoretical and experimental results show that DP3 can simultaneously protect the location privacy of the TBL client and the data privacy of the localization server.