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 …

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 …