Protecting databases from inference attacks

Hinke, Thomas H.; Delugach, Harry S.; Wolf, Randall P.

doi:10.1016/s0167-4048(97)87607-9

Cited by 38 publications

(14 citation statements)

References 1 publication

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“…In particular, we have a specific set of rules that we wish to protect. This is related to inference protection [7], but the problem now extents to non-strict inferences -rules that hold with only some level of support and confidence.…”

Section: O T I V a T I O Nmentioning

confidence: 99%

Using unknowns to prevent discovery of association rules

2001

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A b s t r a c tData mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The efficacy and complexity of this method are discussed. We also present an experiment showing an example of this methodology.

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Section: O T I V a T I O Nmentioning

confidence: 99%

Using unknowns to prevent discovery of association rules

2001

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“…• Multilevel secure inference. 15 The purpose of multilevel secure inference is to ensure that a subset of the database (low data) can't be used to infer exact values of a different subset (high data). • Limiting results.…”

Section: Inference From Data Mining Resultsmentioning

confidence: 99%

Privacy-preserving data mining: why, how, and when

Vaidya

Clifton

2004

IEEE Secur. Privacy Mag.

120

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“…Another key direction of research involves role-based access control. For a sampling of relevant literature on these topics, see [34,71,97,106,165,193,194,195,291,294,295,320,324,325,384,410,411].…”

Section: Large Databases and Inferencementioning

confidence: 99%

Computer science and decision theory

Roberts

2008

Ann Oper Res

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This paper reviews applications in computer science that decision theorists have addressed for years, discusses the requirements posed by these applications that place great strain on decision theory/social science methods, and explores applications in the social and decision sciences of newer decision-theoretic methods developed with computer science applications in mind. The paper deals with the relation between computer science and decision-theoretic methods of consensus, with the relation between computer science and game theory and decisions, and with "algorithmic decision theory."

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Protecting databases from inference attacks

Cited by 38 publications

References 1 publication

Using unknowns to prevent discovery of association rules

Using unknowns to prevent discovery of association rules

Privacy-preserving data mining: why, how, and when

Computer science and decision theory

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