Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals, such as diseases of patients, the credit rating of a customer, and the salary of an employee. Meanwhile, certain information is required to be published. In this paper, we consider data-publishing applications where the publisher specifies both sensitive information and shared information. An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data. The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data. We formulate the inference attack framework, and develop complexity results. We show that computing a safe partial table is an NP-hard problem. We classify the general problem into subcases based on the requirements of publishing information, and propose algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data. The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.
