High-Dimensional Data Release With Local Differential Privacy Under IoT Architecture

local differential privacy (LDP) mechanisms are widely used to collect data generated by IoT sensor devices to protect sensitive information. However, it easily leads to low data utility and high data computing cost due to complex structure and high dimensionality of IoT data. To alleviate this problem, we propose a high-dimensional data publishing method using random responses based on Markov network (HDPRM). This method efficiently conducts the collection and analysis of high-dimensional data under the IoT architecture and satisfies LDP. In particular, it uses the expectation maximization (EM) algorithm to reconstruct the joint distribution of high-dimensional data attributes. Specifically, to improve the effectiveness of data release, we calculate the correlation between attributes and construct corresponding Markov network on the server. Additionally, we cluster high-dimensional attributes through the junction tree algorithm, filter out the joint probability that meets the requirements, and then use this probability to generate synthetic data for publication from the sampled data. Extensive experiments are conducted to comprehensively evaluate the performance of the HDPRM on three real-world datasets. The results show that the method achieves higher data utility under LDP guarantee compared to state-of-the-art methods.