Personal data privacy protection method based onvertical partitioning

In distributed environments, vertical partitioning had been an effective method to protect user privacy. However, current vertical partitioning strategies assumed that there was no collusion among the CSPs (Cloud Service Providers) involved in data storage. This study explored how to protect user data privacy when collusion might exist between CSP. Assuming <italic>n</italic> CSP participated in data storage, with no more than <italic>k</italic> of these potentially colluding, this paper defined a (<italic>k</italic>, <italic>n</italic>)-security for vertical partitioning and introduced an automated computation scheme for vertical partitioning based on machine learning—the MLVP scheme. This MLVP scheme utilized machine learning algorithms to analyze the correlation between attributes, optimized all correlations, and transformed the vertical partitioning problem into a satisfiability problem, which was then solved using a satisfiability solver. Moreover, the security of the MLVP scheme was theoretically analyzed. To validate the effectiveness of the MLVP scheme, experiments were conducted on real datasets to compare the impact of different machine learning algorithms and levels of privacy protection on the effectiveness and performance of the vertical partitioning. The experiments also compared the MLVP scheme with two other schemes that did not consider collusion among CSP, Oriol’s and Ciriani’s schemes, in terms of computation and query speeds. The results showed that the MLVP scheme was slightly slower in computation speed to ensure security against partial CSP collusion. However, it improved the query speed by 32.6% and 8.8% compared to the aforementioned schemes, respectively.