Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Information-Theoretic Disclosure Risk Measures in Statistical Disclosure Control of Tabular Data
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ICDT'05 Proceedings of the 10th international conference on Database Theory
On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge
ACM Transactions on Knowledge Discovery from Data (TKDD)
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
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Sampling is often used to achieve disclosure limitation for categorical and microarray datasets. The motivation is that while the public gets a snapshot of what is in the data, the entire data is not revealed and hence complete disclosure is prevented. However, the presence of prior knowledge is often overlooked in risk assessment. A sample plays an important role in risk analysis and can be used by a malicious user to construct prior knowledge of the domain. In this paper, we focus on formalizing the various kinds of prior knowledge an attacker can develop using samples and make the following contributions. We abstract various types of prior knowledge and define measures of quality which enables us to quantify how good the prior knowledge is with respect to the true knowledge given by the database. We propose a lightweight general purpose sampling framework with which a data owner can assess the impact of various sampling methods on the quality of prior knowledge. Finally, through a systematic set of experiments using real benchmark datasets, we study the effect of various sampling parameters on the quality of prior knowledge that is obtained from these samples. Such an analysis can help the data owner in making informed decisions about releasing samples to achieve disclosure limitation.