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Data-Driven Chinese Walls

Authors Gulsum Akkuzu, Benjamin Aziz

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Gulsum Akkuzu
  • School of Computing, University of Portsmouth, United Kingdom
Benjamin Aziz
  • School of Computing, University of Portsmouth, United Kingdom

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Gulsum Akkuzu and Benjamin Aziz. Data-Driven Chinese Walls. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 3:1-3:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.ICCSW.2018.3

Abstract

Security policy and access control models are often based on qualitative attributes, e.g. security labels, cryptographic credentials. In this paper, we enrich one such model, namely the Chinese Walls model, with quantitative attributes derived from data. Therefore, we advocate a data-driven approach that considers a quantitative definition of access we term, working relations.

Subject Classification

ACM Subject Classification
  • Security and privacy → Access control
Keywords
  • Access Control
  • Big Data
  • Security Policies
  • Chinese Walls Model

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References

  1. Los Alamos National Laboratory: Cyber Security Science. https://csr.lanl.gov/data/. Accessed: 14-06-2018.
  2. D.F.C. Brewer and M.J. Nash. The Chinese Wall Security Policy. In Proceedings of the 1989 IEEE Symposium on Security and Privacy, pages 206-214, Oakland, California, USA, 1989. IEEE Computer Society Press. Google Scholar
  3. Pau–Chen Cheng, Pankaj Rohatgi, Claudia Keser, Paul A. Karger, Grant M. Wagner, and Angela Schuett Reninger. Fuzzy Multi-Level Security:An experiment on quantified risk-adaptive access control. In 2007 IEEE Symposium on Security and Privacy (S&P 2007), 20-23 May 2007, Oakland, California, USA, pages 222-230. IEEE Computer Society, 2007. URL: http://dx.doi.org/10.1109/SP.2007.21.
  4. Dhillon G. and G. Torkzadeh. Value-focused assessment of information system security in organizations. Information Systems Journal, 16(3), 293-314, 2015. Google Scholar
  5. Dieter Gollmann. Computer Security. John Wiley &Son Ltd, 1999. Google Scholar
  6. Aric Hagberg, Alex Kent, Nathan Lemons, and Joshua Neil. Credential hopping in authentication graphs. In 2014 International Conference on Signal-Image Technology Internet-Based Systems (SITIS). IEEE Computer Society, 2014. Google Scholar
  7. Anil K. Jain. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8):651-666, 2010. Google Scholar
  8. McLean John. The algebra of security. In In Security and Privacy, volume 1290, pages 2-7. IEEE Symposium, 1988. Google Scholar
  9. Alexander D. Kent. User-Computer Authentication Associations in Time. Los Alamos National Laboratory, 2014. URL: http://dx.doi.org/10.11578/1160076.
  10. Butler Lampson. A note on the confinement problem. Communications of the ACM, 16(10):613-615, 1973. Google Scholar
  11. S. C. Mouliswaran, C. A. Kumar, and C Chandrasekar. Modeling Chinese wall access control using formal concept analysis. In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. URL: http://dx.doi.org/10.1109/IC3I.2014.7019619.
  12. S. C. Mouliswaran, C. A. Kumar, and C Chandrasekar. Modeling Chinese wall access control using formal concept analysis. In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. URL: http://dx.doi.org/10.1109/IC3I.2014.7019619.
  13. Atluri Vijayalakshmi, Chun Soon, and Mazzoleni Pietro. A Chinese wall security model for decentralized workflow systems. In CCS 2001, Proceedings of the 8th ACM Conference on Computer and Communications Security, Philadelphia, Pennsylvania, USA, November 6-8, 2001., pages 48-57. ACM, 2001. URL: http://dx.doi.org/10.1145/501983.501991.
  14. Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schrödl. Constrained k-means clustering with background knowledge. In Carla E. Brodley and Andrea Pohoreckyj Danyluk, editors, Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 577-584. Morgan Kaufmann, 2001. Google Scholar
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