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Minimal Data Upgrading to Prevent Inference and Association

Steven Dawson, Sabrina De Capitani di Vimercati, Patrick Lincoln, and Pierangela Samarati

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Abstract
Despite advances in recent years in the area of mandatory access control in database systems, today’s information repositories remain vulnerable to inference and data association attacks that can result in serious information leakage. Such information leakage can be prevented by properly classifying information according to constraints that express relationships among the security levels of data objects. In this paper we address the problem of classifying information by enforcing explicit data classification as well as inference and association constraints. We formulate the problem of determining a classification that ensures satisfaction of the constraints, while at the same time guaranteeing that information will not be unnecessarily overclassified. We present an approach to the solution of this problem and give an algorithm implementing it which is linear in simple cases, and low-order polynomial (n^2) in the general case. We also analyze a variant of the problem that is NP-hard.


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BIBTEX

@inproceedings{DBLP:conf/pods/DawsonVLS99,
  author    = {Steven Dawson and
                Sabrina De Capitani di Vimercati and
                Patrick Lincoln and
                Pierangela Samarati},
   title     = {Minimal Data Upgrading to Prevent Inference and Association},
   booktitle = {Proceedings of the Eighteenth ACM SIGACT-SIGMOD-SIGART Symposium
                on Principles of Database Systems, May 31 - June 2, 1999, Philadelphia,
                Pennsylvania},
   publisher = {ACM Press},
   year      = {1999},
   isbn      = {1-58113-062-7},
   pages     = {114-125},
   crossref  = {DBLP:conf/pods/99},
   bibsource = {DBLP, http://dblp.uni-trier.de} } },


























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