An Indoor Localization Mechanisms Based on Local Differential Privacy

Wi-fi fingerprint-based indoor localization is regarded as one of the most promising techniques for location-based services. Services providers would like to train the localization model using the location data. Determining how to protect sensitive information in the indoor localization of users is the main objective. Dealing with the problem of the untrusted third party, we proposed indoor localization mechanisms based on local differential privacy. It extends the local differential privacy theory to a mature machine-learning localization technology to achieve privacy protection while training the localization model. The experiment could control the data quality loss up to 7.2% compared with the central differential privacy. Our investigation suggests that it could provide guidance on indoor localization privacy.