Enhancing Privacy-Preserving Localization by Integrating Random Noise With Blockchain in Internet of Things

Privacy-preserving localization plays a crucial role in enabling various applications on the Internet of Things (IoT). Existing work applies random zero-sum noise to develop privacy-preserving localization, which achieves efficiency and accuracy by adding random noise to preserve private information and cancelling the effect of the noise with the zero-sum characteristic, respectively. However, in practice, some nodes in IoT scenarios may misbehave, not following a pre-defined protocol but adding false noise or tampering with information, which leads to the trust issue for privacy-preserving localization. In this paper, we integrate blockchains with zero-sum noise to achieve trusted privacy-preserving localization against misbehaving nodes. Specifically, a three-layer framework is designed by combining private blockchains with a zero-sum noise-adding mechanism. In the sensing layer, nodes are divided into groups to perform the first noise-adding process to preserve their private location information during location aggregation inside the group. In the blockchain layer, each group constructs the private blockchains to achieve trustworthiness without increasing the risk of privacy leakage and performs the second noise-adding process to protect intermediate information. In the application layer, the target node aggregates the intermediate information from each group to estimate its location. Then, under the framework, we propose an Enhanced Privacy-Preserving Localization (EPPL) algorithm to securely calculate the location of the target node against misbehaving nodes. The correctness, accuracy, privacy, trustworthiness, and efficiency of EPPL are analyzed. The performance of EPPL is evaluated by using simulations. Compared with existing random noise-based methods, it is shown that EPPL can effectively enhance privacy-preserving localization.