Differentiated security levels for personal identifiable information in identity management system

Expert Systems with Applications

Volumn 38, Issue 11, 2011, Pages 14156-14162

  Chen, Jianyong ^a   Wu, Guihua ^a   Shen, Linlin ^a,b   Ji, Zhen ^a  

^a SHENZHEN UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Differentiated security; Identity management; Personal identifiable information

Indexed keywords

DATA SETS; DATA-MINING TOOLS; DIFFERENTIATED SECURITY; EBUSINESS; FORECAST INFORMATION; IDENTITY MANAGEMENT; IDENTITY MANAGEMENT SYSTEMS; ILLEGAL ACCESS; INFORMATION ASSETS; INTERNET SERVICES; KNOWLEDGE BASE; PERSONAL IDENTIFIABLE INFORMATION; PERSONALLY IDENTIFIABLE INFORMATION; RAPID DEVELOPMENT; SECURITY LEVEL; SECURITY REQUIREMENTS; SECURITY THREATS; SERVICE PROVIDER; SIMULATION RESULT; TWO STAGE; USER INFORMATION;

CRIME; DATA MINING; DECISION TREES; INTERNET; KNOWLEDGE BASED SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PLANT EXTRACTS; TELECOMMUNICATION NETWORKS; TREES (MATHEMATICS); WEB SERVICES;

INFORMATION MANAGEMENT;

EID: 79960020592     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.04.226     Document Type: Article

References (27)

1. Acquisti, A.; (2008). Identity Management, Privacy, and Price Discrimination. IEEE Security & Privacy, Vol. 6 (2), pp. 46-50. (Pubitemid 351506255)
2. W. Boehm Barry Software risk management: Principles and practices IEEE Software 8 1 1991 32 41
3. Cantor, S.; Kemp, J.; Philpott, R.; & Maler, E. (2005). Security Assertion Markup Language v2.0. OASIS Security Services Technical Committee Standard, March 2005.
4. Chen, J.; Wang, X.; & He, L. (2008). An Architecture for Differentiated Security Service. In international symposium on electronic commerce and security, Guangzhou, Peoples R China 2008, pp. 301-304.
5. Y.L. Chen, and C.H. Weng Mining fuzzy association rules from questionnaire data Knowledge-based Systems 22 1 2009 46 56
6. H. Chih-Hung Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry Expert Systems with Applications 36 2009 4185 4191
7. C. Chin-Jui, and S. Shiahn-Wern A Study on the application of data mining to disadvantaged social classes in Taiwan's population census Expert Systems with Applications 36 2009 510 518
8. R. Cunha, P. Adeodato, and S. Meira Knowledge reuse in data mining projects and its practical applications Enterprise Information Systems 24 2009 317 324
9. http://www.ics.uci.edu/m̃ learn
10. J. Huang, and C.X. Ling Using AUC and accuracy in evaluating learning algorithms IEEE Transactions on Knowledge and Data Engineering 17 3 2005 299 310 (Pubitemid 40320164)
11. O.L. Mangasarian, and E.W. Wild Nonlinear knowledge-based classification IEEE Transactions on Neural Networks 19 10 2008 1826 1832
12. H. Marit, S. Ari, and C. Alissa Privacy and identity management IEEE Security & Privacy 6 2 2008 38 45
13. M.C. Mont Dealing with privacy obligations: Important aspects and technical approaches 2004 Zaragoza Spain In 1st international conference on trust and privacy in digital business, pp. 120-131
14. E.W.T. Ngai, L. Xiu, and D.C.K. Chau Application of data mining techniques in customer relationship management: A literature review and classification Expert Systems with Applications 36 2009 2592 2602
15. D. Perera, J. Kay, I. Koprinska, K. Yacef, and O. Zaiane Clustering and sequential pattern mining of online collaborative learning data IEEE Transactions on Knowledge and Data Engineering 21 6 2009 759 772
16. Quinlan, J. R. (1993). Program for Machine Learning. San Mateo, CA: Morgan Kaufmann.
17. ITU-T Recommendation SG17 TD0130 (2009). Criteria for assessing the level of protection for personally identifiable information in identity management, ITU-T Standard Organization.
18. L. Rokach, R. Romano, and O. Maimon Mining manufacturing databases to discover the effect of operation sequence on the product quality Journal of Intelligent Manufacturing 19 3 2008 313 325
19. V. Speckauskiene, and A. Lukosevicius Methodology of adaptation of data mining methods for medical decision support: Case study Elektronikair Elektrotechnika 2 2009 25 28
20. C.W. Thompson, and D.R. Thompson Identity management IEEE Internet Computing 11 3 2007 82 85 (Pubitemid 46736846)
21. F. Tom An introduction to ROC analysis Pattern Recognition Letters 27 2006 861 874
22. The University of Waikato. WEKA Machine Learning Project. http://www.cs.waikato.ac.nz/ml/weka/.
23. WS-Federation (2006). http://specs.xmlsoap.org/ws/2006/12/federation/, December 2006.
24. WS-Trust Specification (2007). http://docs.oasis-open.org/ws-sx/ws-trust/ 200512, OASIS Standard, WS-Trust 1.3, March 2007.
25. Zhao, X. A. (2008). Study of Performance Evaluation of HRM: Based on Data Mining. 2008 International Seminar on Future Information Technology and Management Engineering. Leicestershire, England pp. 45-48.
26. J.H. Yang, J.J. Zhao, Y.H. Zhu, W. Xiong, J.Y. Lin, and X. Ma Identification of candidate cancer genes involved in human retinoblastoma by data mining Childs Nervous System 24 2008 893 900
27. L. Yan-Ru, W. Leuo-Hong, and H. Chao-Fu Extracting the significant-rare keywords for patent analysis Expert Systems with Applications 36 2009 5200 5204