Aiming at the problem that the existing differential privacy high-dimensional data publishing methods are difficult to take into account both the complex attribute correlation between data and computational cost, a differential privacy high-dimensional data publishing method based on clustering analysis technology, namely PrivBC, was proposed. Firstly, the attribute clustering method was designed based on the K-means++, the maximum information coefficient was introduced to quantify the correlation between the attributes, and the data attributes with high correlation were clustered. Secondly, for each data subset obtained by the clustering, the correlation matrix was calculated to reduce the candidate space of attribute pairs, and the Bayesian network satisfying differential privacy was constructed. Finally, each attribute was sampled according to the Bayesian networks, and a new private dataset was synthesized for publishing. Compared with PrivBayes method, PrivBC method had the misclassification rate and running time reduced by 12.6% and 30.2% averagely and respectively. Experimental results show that the proposed method can significantly improve the computational efficiency with ensuring the data availability, and provides a new idea for the private publishing of high-dimensional big data.

The existing privacy protection technologies rarely consider query probability, map data, semantic information of Point of Information (POI) and other side information, so the attacker can deduce the privacy information of the user by combining the side information with the location data. To resolve this problem, a new algorithm was proposed to protect the location privacy of users, namely ARB (Anonymouse Region Building). Firstly, the space was divided into grids, and historical statistics were utilized to obtain the probability of queries for each grid of space. Then, the anonymous region for each user was obtained based on query probability of corresponding grid to protect the user's location privacy information. Finally, the location information entropy was used as a measure of privacy protection performance, and the performance of the proposed method was verified by comparison with the existing two methods on the real data set. The experimental results show that ARB obtains better privacy protection effect and lower computation complexity.