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Data Security and Privacy in Data-Intensive Computing Clusters

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Abstract

Data security is of utmost importance, especially in cases when improper disclosure would compromise corporate trade secrets, expose Personal Identifiable Information (PII) and lead to fines and legal recourse

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References

  1. J.D. Meier, A. Mackman, and B. Wastell, “How to: Create a Threat Model for a Web Application at Design Time,” http://msdn.microsoft.com/en-us/library/ff647894.aspx

  2. M. Wilson, J. Hash, “Building an Information Technology Security Awareness and Training Program,” http://csrc.nist.gov/publications/nistpubs/800-50/NIST-SP800-50.pdf

  3. “Information technology — Security techniques — Code of practice for information security management,” http://www.iso27001security.com/html/27002.html

  4. A. Evfimievski, “Randomization in privacy preserving data mining,” ACM SIGKDD Explorations Newsletter, Vol. 4, No. 2, Dec. 2002.

    Google Scholar 

  5. Y. Zhu, and L. Liu, “Optimal randomization for privacy preserving data mining,” Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004.

    Google Scholar 

  6. A.S. Narayanan, “How to break anonymity of the Netflix prize data set,” http://arxiv.org/abs/cs/0610105, 2007.

  7. K. Knorr and H. Weidner, “Analyzing Separation of Duties in Petri Net Workflows,” Department of Information Technology University of Zurich, 2001.

    Google Scholar 

  8. J.A. Halderman, S.D. Shoen, N. Heninger, W. Paul, J.A. Calandrino, A.J. Feldman, J. Appelbaum, and E.W. Felten “Lest we remember: cold-boot attacks on encryption keys,” Communications of the ACM – Security in the Browser, Vol. 52, No. 5, May 2009.

    Google Scholar 

  9. R. Kissel, M. Scholl, S. Skolochenko, and X. Li, “Guidelines for Media Sanitization,” http://csrc.nist.gov/publications/nistpubs/800-88/NISTSP800-88_rev1.pdf

  10. Intel Corp. “Trusted Execution Technology” http://www.intel.com/technology/malwarer http://eduction/index.htm

  11. R. Araujo, “Microsoft MVP – Data Validation – Step One in Improving the Security of Your Web Applications,” http://technet.microsoft.com/en-us/library/dd699463.aspx

  12. J. Domingo-Ferrer and V. Torra2, “Aggregation Techniques for Statistical Confidentiality,” http://vneumann.etse.urv.es/webCrises/publications/bcpi/domingotorra_calvomesiar.pdf

  13. B. Prince, “How attackers use social networks,” http://securitywatch.eweek.com/botnet_cncs/how_attackers_use_social_networks_for_command_and_control_operations.html

  14. Cloud Security Alliance, “Cloud Security Matrix,” https://cloudsecurityalliance.org/research/projects/cloud-controls-matrix-ccm/

  15. Cloud Security Alliance, “Top Threats to Cloud Computing,” https://cloudsecurityalliance.org/topthreats/csathreats.v1.0.pdf

  16. C. Gentry, and S. Halevi, “A working implementation of fully homomorphic encryption,” http://eurocrypt2010rump.cr.yp.to/9854ad3cab48983f7c2c5a2258e27717.pdf, EuroCrypt, 2010.

  17. J.H. Langbein ‘Questioning the Trust Law Duty of Loyalty’ (2005) 114 Yale Law Journal 929–990.

    Google Scholar 

  18. J.E. West “LexisNexis brings its data management magic to bear on scientific data,” http://www.hpcwire.com/hpcwire/2009-07-23/lexisnexis_brings_its_data_management_magic_to_bear_on_scientific_data.html, HPC Wire, 2009.

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Correspondence to Flavio Villanustre .

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Villanustre, F., Robinson, J. (2011). Data Security and Privacy in Data-Intensive Computing Clusters. In: Furht, B., Escalante, A. (eds) Handbook of Data Intensive Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1415-5_17

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  • DOI: https://doi.org/10.1007/978-1-4614-1415-5_17

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  • Publisher Name: Springer, New York, NY

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