Designing Customizable Privacy-Preserving Localization in Internet of Things

Localization technology plays an important role in many Internet of Things (IoT) applications. The requirements of privacy preservation levels may vary depending on the application scenario. However, most of the existing privacy-preserving localization schemes adopt a fixed privacy preservation level, which can not meet the requirements of customizable privacy-preserving localization. In order to solve the issue, this paper designs a customizable privacy-preserving localization algorithm (CPPL). Firstly, by setting three levels of privacy preservation, anchor node users can choose to customize their privacy protection level based on their own privacy protection requirements. The privacy level noise they need is calculated, and the noise-added location information is sent to the target node. Secondly, to preserve the private noise level, the anchor node sends the privacy level noise plus zero-sum noise to the target node. The target node calculates and infers its own location by using the location information with added privacy level noise sent by the anchor node and the privacy level noise information after adding zero-sum noise. The experimental results show that the designed algorithm ensures privacy protection and localization accuracy while meeting the user's customizable requirements.