DESIGN & DEVELOPMENT OF SUITABLE METHODOLOGIES AND SYSTEMS FOR HANDLING SENSITIVE DATA IN CLOUD COMPUTING ENVIRONMENT

Prof.Dr.G.Manoj Someswar, K.Madhavi Latha

Abstract


Cloud computing others the prospect of on-demand, elastic computing, provided as a utility service, and it is revolutionizing many domains of computing. Compared with earlier methods of processing data, cloud computing environments provide significant benefits, such as the availability of auto-mated tools to assemble, connect, configure and reconfigure virtualized re-sources on demand. These make it much easier to meet organizational goals as organizations can easily deploy cloud services. However, the shift in paradigm that accompanies the adoption of cloud computing is increasingly giving rise to security and privacy considerations relating to facets of cloud computing such as multi-tenancy, trust, loss of control and accountability. Consequently, cloud platforms that handle sensitive information are required to deploy technical measures and organizational safeguards to avoid data protection break-downs that might result in enormous and costly damages. Sensitive information in the context of cloud computing encompasses data from a wide range of different areas and domains. Data concerning health is a typical example of the type of sensitive information handled in cloud computing environments, and it is obvious that most individuals will want information related to their health to be secure. Hence, with the growth of cloud computing in recent times, privacy and data protection requirements have been evolving to protect individuals against surveillance and data disclosure. Some examples of such protective legislation are the EU Data Protection Directive (DPD) and the US Health Insurance Portability and Accountability Act (HIPAA), both of which demand privacy preservation for handling personally identifiable information.

There have been great efforts to employ a wide range of mechanisms to enhance the privacy of data and to make cloud platforms more secure. Techniques that have been used include: encryption, trusted platform module, secure multi-party computing, homomorphic encryption, anonymization, container and sandboxing technologies. However, it is still an open problem about how to correctly build usable privacy-preserving cloud systems to handle sensitive data securely due to two research challenges. First, existing privacy and data protection legislation demand strong security, transparency and audibility of data usage. Second, lack of familiarity with a broad range of emerging or existing security solutions to build efficient cloud systems. This research work focuses on the design and development of several systems and methodologies for handling sensitive data appropriately in cloud computing environments.

The key idea behind the proposed solutions is en-forcing the privacy requirements mandated by existing legislation that aims to protect the privacy of individuals in cloud-computing platforms. We begin with an overview of the main concepts from cloud computing, followed by identifying the problems that need to be solved for secure data management in cloud environments. It then continues with a description of background material in addition to reviewing existing security and privacy solutions that are being used in the area of cloud computing. Our first main contribution is a new method for modelling threats to privacy in cloud environments which can be used to identify privacy requirements in accordance with data protection legislation. This method is then used to propose a framework that meets the privacy requirements for handling data in the area of genomics. That is, health data concerning the genome (DNA) of individuals. Our second contribution is a system for preserving privacy when publishing sample availability data. This system is noteworthy because it is capable of cross-linking over multiple datasets. The research work continues by proposing a system called ScaBIA for privacy-preserving brain image analysis in the cloud. The outcome of our research work describes a new approach for quantifying and minimizing the risk of operating system kernel exploitation, in addition to the development of a system call interposition reference monitor for Lind - a dual sandbox.

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References


. R. A. S. NIST Big Data Public Working Group, “DRAFT NIST Big Data Interoperability Framework,” April 2015. Accessed July 2015.

. F. Liu, J. Tong, J. Mao, R. Bohn, J. Messina, L. Badger, and D. Leaf, NIST Cloud Computing Reference Architecture: Recommendations of the National Institute of Standards and Technology (Special Publication 500-292). USA: Create Space Independent Publishing Platform, 2012.

. B. Russell, “Realizing Linux Containers (LXC).” http://www.slideshare. net/BodenRussell/. Accesed October 2015.

. L. Sweeney, “Simple Demographics Often Identify People Uniquely,” Carnegie Mellon University, Pittsburg, Working Paper 3, 2000.

. S. Rusitschka and A. Ramirez, “Big Data Technologies and Infrastructures.” http://byte-project.eu/research/, Sept. 2014. Deliverable D1.4, Version 1.1.

. P. Mell and T. Grance, “The NIST Definition of Cloud Computing.” http: //www.csrc.nist.gov/groups/SNS/cloud-computing/, July 2009.

. M. Hogan, F. Liu, and A. Sokol, “Nist cloud computing standards roadmap,” 2011.

. E. U. Directive, “95/46/EC of the European Parliament and of the Council of 24 October 1995 on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of such Data,” 1995.

. U. States., “Health insurance portability and accountability act of 1996 [micro form] : conference report (to accompany h.r. 3103).” http://nla.gov.au/ nla.cat-vn4117366, 1996.

. R.-M. Åhlfeldt, “Information security in distributed healthcare: Exploring the needs for achieving patient safety and patient privacy,” 2008.

. 11.S. Pearson, “Privacy, security and trust in cloud computing,” in Privacy and Security for Cloud Computing (S. Pearson and G. Yee, eds.), Computer Communications and Networks, pp. 3–42, Springer London, 2013.

. A. Cavoukian, “The Security-Privacy Paradox: Issues, misconceptions, and Strategies.” https://www.ipc.on.ca/images/Resources/sec-priv. pdf, 2003. Accessed November 2015.

. United Nations, “The Universal Declaration of Human Rights.” http://www. un.org/en/documents/udhr/index.shtml, 1948. Accessed August 2015.

. A. Westin, Privacy and Freedom. New York Atheneum, 1967.

. 15.U. States., “Gramm-leach-bliley act.” http://www.gpo.gov/fdsys/pkg/ PLAW-106publ102/pdf/PLAW-106publ102.pdf, November 1999.

. 16. U. S. F. Law, “Right to financial privacy act of 1978.” https://epic.org/ privacy/rfpa/, 1978.“Telecommunications Act of 1996.” http://transition.fcc.gov/Reports/ tcom1996.pdf, 1996. No. 104-104, 110 Stat. 56.

. 17. D. Bigo, G. Boulet, C. Bowden, S. Carrera, J. Jeandesboz, and A. Scherrer, “Fighting cyber crime and protecting privacy in the cloud.” European Parliament, Policy Department C: Citizens’ Rights and Constitutional Affairs, http://www.europarl.europa.eu/committees/en/ studiesdownload.html?languageDocument=EN&file=79050, Oct. 2012.

. 18. S. Stalla-Bourdillon, “Liability exemptions wanted! internet intermediaries’ liability under uk law,” Journal of International Commercial Law and Technology, vol. 7, no. 4, 2012. “Scalable, secure storage bio bank.” http://www.biobankcloud.eu. EU FP7 Framework, Grant Agreement No: 317871, Accessed January 2015.

. 19. M. Janitz, ed., Next-generation genome sequencing: towards personalized medicine. John Wiley & Sons, 2011.

. 20. R. Weissleder and M. Y. Pittet, “Imaging in the era of molecular oncology,” Nature, vol. 452, no. 7187, 2008.


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