A Numerical Splitting and Adaptive Privacy Budget-Allocation-Based LDP Mechanism for Privacy Preservation in Blockchain-Powered IoT

Blockchain has gradually attracted widespread attention from the research community of the IoT, due to its decentralization, consistency, and other attributes. It builds a secure and robust system by generating a backup locally for each participant node to collectively maintain the network. However, this feature brings some privacy concerns since all nodes can access the chain data, users' sensitive information under risk of leakage. The local differential privacy (LDP) mechanism can be a promising way to address this issue as it implements data perturbation before uploading to the chain. While traditional LDP mechanisms cannot fit well with the blockchain since the requirements of a fixed input range, large data volume, and using the same privacy budget, which are practically difficult in a decentralized environment. To overcome these problems, we propose a novel LDP mechanism to split input numerical data and implement perturbation by digital bits, which does not require a fixed input range and large data volume. In addition, we use an iteration approach to adaptively allocate the privacy budget for different perturbation procedures that minimize the total deviation of perturbed data and increase the data utility. We employ mean estimation as the statistical utility metric under the same and randomized privacy budgets to evaluate the performance of our novel LDP mechanism. The experiment results indicate that the proposed LDP mechanism performs better in different scenarios, and our adaptive privacy budget allocation model can significantly reduce the deviation of the perturbation function to provide high data utility while maintaining privacy.