INTERNET SECURITY APPLICATIONS OF GRÖBNER-SHIRSHOV BASES

2010 ◽  
Vol 03 (03) ◽  
pp. 435-442
Author(s):  
Andrei Kelarev ◽  
John Yearwood ◽  
Paul Watters
Keyword(s):  
Data Mining ◽  
Present Article ◽  
Internet Security ◽  
General Algorithm ◽  
Standard Approach ◽  
Security Applications ◽  
Multiple Classifiers ◽  
Essential Properties

This article is motivated by internet security applications of multiple classifiers designed for the detection of malware. Following a standard approach in data mining, Dazeley et al. (Asian-European J. Math. 2 (2009)(1) 41–56) used Gröbner-Shirshov bases to define a family of multiple classifiers and develop an algorithm optimizing their properties.The present article complements and strengthens these results. We consider a broader construction of classifiers and develop a new and more general algorithm for the optimization of their essential properties.

Privacy-Preserving Data Mining

2008 ◽  
pp. 693-704
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

Privacy-Preserving Data Mining

2008 ◽  
pp. 627-638 ◽  
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

scholarly journals A POLYNOMIAL RING CONSTRUCTION FOR THE CLASSIFICATION OF DATA

2009 ◽  
Vol 79 (2) ◽  
pp. 213-225 ◽  
Author(s):  
A. V. KELAREV ◽  
J. L. YEARWOOD ◽  
P. W. VAMPLEW
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Science ◽  
Polynomial Ring ◽  
Learning Tools ◽  
Nice Property ◽  
Multiple Classifiers ◽  
Tools And Techniques ◽  
Quotient Rings

AbstractDrensky and Lakatos (Lecture Notes in Computer Science, 357 (Springer, Berlin, 1989), pp. 181–188) have established a convenient property of certain ideals in polynomial quotient rings, which can now be used to determine error-correcting capabilities of combined multiple classifiers following a standard approach explained in the well-known monograph by Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques (Elsevier, Amsterdam, 2005)). We strengthen and generalise the result of Drensky and Lakatos by demonstrating that the corresponding nice property remains valid in a much larger variety of constructions and applies to more general types of ideals. Examples show that our theorems do not extend to larger classes of ring constructions and cannot be simplified or generalised.

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