ILIAD: An Integrated Laboratory for Inference Analysis and Detection

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

doi:10.1007/978-0-387-34932-9_21

IFIP Advances in Information and Communication Technology

1996

DOI: 10.1007/978-0-387-34932-9_21

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ILIAD: An Integrated Laboratory for Inference Analysis and Detection

Thomas H. Hinke

Harry S. Delugach

Randall P. Wolf

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Cited by 3 publications

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“…Our work can be easily extended to handle multiple tables, although, as shown by[5], a database with multiple tables can be transformed into one with a single "universal" table; see also[19, section 2].…”

mentioning

confidence: 99%

A model-theoretic approach to data anonymity and inference control

Arkoudas¹,

Vashist²

2012

Proceedings of the Second ACM Conference on Data and Application Security and Privacy

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In secure data management the inference problem occurs when data classified at a high security level becomes inferrible from data classified at lower levels. We present a model-theoretic approach to this problem that captures the epistemic state of the database user as a set of possible worlds or models. Privacy is enforced by requiring the existence of k > 1 models assigning distinct values to sensitive attributes, and implemented via model counting. We provide an algorithm mechanizing this process and show that it is sound and complete for a large class of queries.

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mentioning

confidence: 99%

A model-theoretic approach to data anonymity and inference control

Arkoudas¹,

Vashist²

2012

Proceedings of the Second ACM Conference on Data and Application Security and Privacy

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Protecting Sensitive Business Information While Sharing Serial-Level Data

Zanetti

Čapkun

2008

2008 12th Enterprise Distributed Object Computing Conference Workshops

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Catalytic inference analysis: detecting inference threats due to knowledge discovery

Hale

Shenoi²

Proceedings. 1997 IEEE Symposium on Security and Privacy (Cat. No.97CB36097)

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Knowledge discovery in databases can be enhanced b y zntroducang '%atalytic relations" conveying external knowledge. The new information catalyzes database inference, manifesting latent channels. Catalytic inference as zniprecase in nature, but the granularity of inference may be fine enough to create security conipromises . Cat a1 yt ic inference is coinpu t a t ion a1 1 y intensive. However, it can be automated by advanced search engines that gather and assemble knowledge from information repositories. The relentless inforination gathering potential of such search engines makes them forniidable security threats. This paper presents a formalism for modeling and analyzing catalytic inference in "mixed" databases containing vartous precise, zmprecise and fuzzy relations. The iriference forrnalisrri is flexible and robust, and well-suited to amplementation.

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ILIAD: An Integrated Laboratory for Inference Analysis and Detection

Cited by 3 publications

References 5 publications

A model-theoretic approach to data anonymity and inference control

A model-theoretic approach to data anonymity and inference control

Protecting Sensitive Business Information While Sharing Serial-Level Data

Catalytic inference analysis: detecting inference threats due to knowledge discovery

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