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

Abstract

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.