Privacy-Preserving WiFi Localization Based on Inner Product Encryption in a Cloud Environment

Cloud-based indoor positioning services have advantages over non-cloud methods but also confront serious privacy concerns. Existing privacy-preserving schemes are designed for conventional two-entity localization models thus not applicable to the cloud-based indoor positioning services involving three entities. In addition, these methods incur high computational and communication overhead. To tackle these issues, we proposed a privacy-preserving indoor positioning scheme for WiFi localization based on Inner Product Encryption in a cloud environment. A bloom filter constructed with Locality Sensitive Hashing was designed to map WiFi fingerprints from Euclidean to inner product space with the distance relationships maintained for converting the location estimation to inner product calculations. Inner Product Encryption protects the user's fingerprint and database information held by the positioning service provider. Fingerprint similarity as determined by the inner product is decrypted on the cloud to retrieve the closest encrypted location coordinates for users. In addition, a retrieval structure based on Hierarchical Navigable Small World graph was designed to improve efficiency. Theoretical analysis and experimental results demonstrate that the scheme has low computational and communication overhead while ensuring security and not significantly degrading the localization accuracy. Moreover, the overhead does not increase significantly with database size thus this approach is highly scalable.