Data and Applications Security and Privacy XXIV: 24th Annual by Shiyuan Wang, Divyakant Agrawal, Amr El Abbadi (auth.), Sara

By Shiyuan Wang, Divyakant Agrawal, Amr El Abbadi (auth.), Sara Foresti, Sushil Jajodia (eds.)

This e-book constitutes the complaints of the twenty fourth Annual IFIP WG 11.3 operating convention on information and functions protection, held in Rome Italy in June 2010. The 18 complete and eleven brief papers provided during this quantity have been conscientiously reviewed and chosen from sixty one submissions. the subjects coated are question and information privateness; info defense; entry keep watch over; info confidentiality and question verification; coverage definition and enforcement; and belief and identification management.

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Extra info for Data and Applications Security and Privacy XXIV: 24th Annual IFIP WG 11.3 Working Conference, Rome, Italy, June 21-23, 2010. Proceedings

Sample text

Inf. Syst. Secur. 12(1) (2008) 22. : Flexible support for multiple access control policies. ACM TODS 26(2), 214–260 (2001) 23. : IT-Security and Privacy. edu Abstract. Publishing decision trees can provide enormous benefits to the society. Meanwhile, it is widely believed that publishing decision trees can pose a potential risk to privacy. However, there is not much investigation on the privacy consequence of publishing decision trees. To understand this problem, we need to quantitatively measure privacy risk.

In this case, A says s means that A has caused s to be said, that s has been said on A’s behalf, or that A supports s. We assume that such assertions are understood by a reference monitor in charge of making decisions on access to a resource r. The reference monitor 20 S. Barker and V. Genovese may implement the policy that a particular data requester A is authorized to perform action a on resource r that contains “private data”. 1 Similarly, a request for the operation a on r from a principal B may be represented by the formula: B says do on(a, r).

Very likely, many solutions exist. However, we are not interested in finding just any solution, we are interested in finding a solution that achieves the most unbiased estimate of P (SA | QI). This is exactly what can be achieved by using the maximum entropy theory. Based on this well-established theory, we propose a systematic method to quantify the privacy disclosure risk in decision trees. The focus of this method is how to formulate constraints from all the information available to adversaries.

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