A Fast Algorithm for Detecting Second Paths in Database Inference Analysis*

Hinke, Thomas H.; Delugach, Harry S.; Chandrasekhar, Asha

doi:10.3233/jcs-1994/1995-32-304

Cited by 19 publications

(8 citation statements)

References 6 publications

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“…Techniques to detect privacy violations via inference channels in MLS relational databases fall into two general categories. The first category includes techniques that detect inference channels during database design time [12,26,27,34,35,43,46,48,60,63,66]. Inference channels are typically removed by modifying the database design and/or by increasing the classification levels of some of the data items.…”

Section: Related Workmentioning

confidence: 99%

Unauthorized inferences in semistructured databases

Farkas¹,

Brodsky²,

Jajodia³

2006

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Unauthorized inferences in semistructured databases

Farkas¹,

Brodsky²,

Jajodia³

2006

Information Sciences

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“…They considered computing intersection, intersection size, equijoin and equijoin size, assuming that certain metadata such as the cardinality of databases can be shared to both parties. Besides, there exists an extensive literature on inference control in multilevel secure databases [9,15,14,13,19]. All these works prohibit the sharing of databases.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving data mashup

Mohammed

Fung

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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Mashup is a web technology that combines information from more than one source into a single web application. This technique provides a new platform for different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In this paper, we study and resolve a real-life privacy problem in a data mashup application for the financial industry in Sweden, and propose a privacy-preserving data mashup (PPMashup) algorithm to securely integrate private data from different data providers, whereas the integrated data still retains the essential information for supporting general data exploration or a specific data mining task, such as classification analysis. Experiments on real-life data suggest that our proposed method is effective for simultaneously preserving both privacy and information usefulness, and is scalable for handling large volume of data.

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“…Techniques to detect and remove inference channels can be organized into two categories: The first category includes techniques that detect inference channels during database design; any channels are removed by modifying the database design and/or by increasing the classification levels of some of the data items [2], [6], [7], [12], [17], [9], [13], [14], [16], [3], [4]. These techniques often result in overclassification of data and, therefore, reduce the availability of data.…”

Section: Related Work and Their Limitationsmentioning

confidence: 99%

Secure databases: constraints, inference channels, and monitoring disclosures

Brodsky

Farkas

Jajodia

2000

IEEE Trans. Knowl. Data Eng.

130

122

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AbstractÐThis paper investigates the problem of inference channels that occur when database constraints are combined with nonsensitive data to obtain sensitive information. We present an integrated security mechanism, called the Disclosure Monitor, which guarantees data confidentiality by extending the standard mandatory access control mechanism with a Disclosure Inference Engine. The Disclosure Inference Engine generates all the information that can be disclosed to a user based on the user's past and present queries and the database and metadata constraints. The Disclosure Inference Engine operates in two modes: data-dependent mode, when disclosure is established based on the actual data items, and data-independent mode, when only queries are utilized to generate the disclosed information. The disclosure inference algorithms for both modes are characterized by the properties of soundness (i.e., everything that is generated by the algorithm is disclosed) and completeness (i.e., everything that can be disclosed is produced by the algorithm). The technical core of this paper concentrates on the development of sound and complete algorithms for both datadependent and data-independent disclosures.

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A Fast Algorithm for Detecting Second Paths in Database Inference Analysis*

Cited by 19 publications

References 6 publications

Unauthorized inferences in semistructured databases

Unauthorized inferences in semistructured databases

Privacy-preserving data mashup

Secure databases: constraints, inference channels, and monitoring disclosures

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