Learning in the presence of malicious errors
SIAM Journal on Computing
A General Additive Data Perturbation Method for Database Security
Management Science
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Generating Sufficiency-based Non-Synthetic Perturbed Data
Transactions on Data Privacy
A Bayesian model for disclosure control in statistical databases
Data & Knowledge Engineering
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This paper examines privacy protection in a statistical database from the perspective of an intruder using learning theory to discover private information. With the rapid development of information technology, massive data collection is relatively easier and cheaper than ever before. The challenge is how to provide database users with reliable and useful data while protecting the privacy of the confidential information. This paper discusses how to prevent disclosing the identity of unique records in a statistical database. The authors' research extends previous work and shows how much protection is necessary to prevent an adversary from discovering confidential data with high probability at small error.