Controlling logical inference in multilevel database systems

Morgenstern, Matthew

doi:10.1109/secpri.1988.8116

Cited by 69 publications

(20 citation statements)

References 11 publications

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“…Therefore, different probabilities should be assigned to different individuals to convey this probabilistic nature of identity inference. Denning and Morgenstern [24][25][26] were the first to use information entropy to predict the risk of such probabilistic inferences in multilevel databases. Given two data items x and y, let H(y) denote the entropy of y and H x (y) denote the conditional entropy of y given x.…”

Section: The Challenge Of Measuring Anonymitymentioning

confidence: 99%

Online Anonymity Protection in Computer-Mediated Communication

Motahari

Ziavras

Jones

2010

IEEE Trans.Inform.Forensic Secur.

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In any situation where a set of personal attributes are revealed, there is a chance that revealed data can be linked back to its owner. Examples of such situations are publishing user profile micro-data or information about social ties, sharing profile information on social networking sites, or revealing personal information in computer-mediated communication. Measuring user anonymity is the first step to ensuring that the identity of the owner of revealed information cannot be inferred. Most current measures of anonymity ignore important factors such as the probabilistic nature of identity inference, the inferrer's outside knowledge, and the correlation between user attributes. Furthermore, in the social computing domain variations in personal information and various levels of information exchange among users make the problem more complicated. We present an information-entropy-based realistic estimation of the user anonymity level to deal with these issues in social computing in an effort to help predict \identity inference risks. We then address implementation issues of online protection by proposing complexity reduction methods that take advantage of basic information entropy properties. Our analysis and delay estimation based on experimental data show that our methods are viable, effective and efficient in facilitating privacy in social computing and synchronous computer-mediated communications.

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Section: The Challenge Of Measuring Anonymitymentioning

confidence: 99%

Online Anonymity Protection in Computer-Mediated Communication

Motahari

Ziavras

Jones

2010

IEEE Trans.Inform.Forensic Secur.

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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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“…Detecting and preventing the disclosure of sensitive data via inference channels is referred to as the inference problem [9]. Solutions to the inference problem can be categorized as either a database design [2,3,7,8,11,14,15,17,18,21] or a query processing [4,10,12,13,16,19] solution.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Disclosure Monitor (D 2 Mon): An Improved Query Processing Solution

Toland

Farkas

Eastman

2005

Lecture Notes in Computer Science

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Abstract. The Dynamic Disclosure Monitor (D2 Mon) is a security mechanism that executes during query processing time to prevent sensitive data from being inferred. A limitation of D 2 Mon is that it unnecessarily examines the entire history database in computing inferences. In this paper, we present a process that can be used to reduce the number of tuples that must be examined in computing inferences during query processing time. In particular, we show how a priori knowledge of a database dependency can be used to reduce the search space of a relation when applying database dependencies. Using the database dependencies, we develop a process that forms an index table into the database that identifies those tuples that can be used in satisfying database dependencies. We show how this process can be used to extend D 2 Mon to reduce the number of tuples that must be examined in the history database when computing inferences. We further show that inferences that are computed by D 2 Mon using our extension are sound and complete.

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Controlling logical inference in multilevel database systems

Cited by 69 publications

References 11 publications

Online Anonymity Protection in Computer-Mediated Communication

Online Anonymity Protection in Computer-Mediated Communication

Computer science and decision theory

Dynamic Disclosure Monitor (D 2 Mon): An Improved Query Processing Solution

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