Journal of Applied Mathematics

Service Outsourcing Character Oriented Privacy Conflict Detection Method in Cloud Computing

Changbo Ke, Zhiqiu Huang, Weiwei Li, Yi Sun, and Fangxiong Xiao

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

Cloud computing has provided services for users as a software paradigm. However, it is difficult to ensure privacy information security because of its opening, virtualization, and service outsourcing features. Therefore how to protect user privacy information has become a research focus. In this paper, firstly, we model service privacy policy and user privacy preference with description logic. Secondly, we use the pellet reasonor to verify the consistency and satisfiability, so as to detect the privacy conflict between services and user. Thirdly, we present the algorithm of detecting privacy conflict in the process of cloud service composition and prove the correctness and feasibility of this method by case study and experiment analysis. Our method can reduce the risk of user sensitive privacy information being illegally used and propagated by outsourcing services. In the meantime, the method avoids the exception in the process of service composition by the privacy conflict, and improves the trust degree of cloud service providers.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 240425, 11 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305769

Digital Object Identifier
doi:10.1155/2014/240425

Citation

Ke, Changbo; Huang, Zhiqiu; Li, Weiwei; Sun, Yi; Xiao, Fangxiong. Service Outsourcing Character Oriented Privacy Conflict Detection Method in Cloud Computing. J. Appl. Math. 2014 (2014), Article ID 240425, 11 pages. doi:10.1155/2014/240425. https://projecteuclid.org/euclid.jam/1425305769


Export citation

References

  • P. Mell and T. Grance, Draft NIST working definition of cloud computing, 2009, http://csrc.nist.gov/groups/SNS/cloud- computing/index.html.
    URL: Link to item
  • M. Armbrust, A. Fox, R. Griffith et al., “Above the clouds: a berkeley view of cloud computing,” Tech. Rep. UCB-EECS-28, University of California, Berkeley, Calif, USA, 2009.
  • L. D. Brandeis and S. D. Warren, The Right to Privacy, edited by Steven Alan Childress, 2010.
  • I. Goldberg, D. Wagner, and E. Brewer, “Privacy-enhancing technologies for the internet,” in Proceedings of the 42nd IEEE International Computer Conference(COMPCON '97), pp. 103–109, 1997.
  • F. Xiao, Z. Huang, Z. Cao, J. Hu, and L. Liu, “Modeling cost-aware web services composition using PTCCS,” in Proceedings of the IEEE International Conference on Web Services (ICWS '09), pp. 461–468, 2009.
  • G. Yee and L. Korba, “Privacy policy compliance for web services,” in Proceedings of the IEEE International Conference on Web Services (ICWS '04), pp. 158–165, 2004.
  • J. Zhang, C. K. Chang, L.-J. Zhang, and P. C. K. Hung, “Toward a service-oriented development through a case study,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 37, no. 6, pp. 955–969, 2007.
  • S. Pearson, “Taking account of privacy when designing cloud computing services,” in Proceedings of the ICSE Workshop on Software Engineering (CLOUD '09), IEEE Computer Society, Vancouver, Canada, 2009, HP Labs Technical Report, HPL-54.
  • S. Pearson and A. Charlesworth, “Accountability as a way forward for privacy protection in the cloud,” HP Labs Technical Report HPL–178, 2009.
  • S. Pearson, V. Tountopoulos, D. Catteddu et al., “Accountability for cloud and other future Internet services,” in Proceedings of the IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom '12), pp. 629–632, 2012.
  • I. Roy, H. E. Ramadan, S. T. V. Setty et al., “Airavat: security and privacy for MapReduce,” in Proceedings of the 7th Usenix Symposium on Networked Systems Design and Implementation, M. Castro, Ed., pp. 297–2312, USENIX Association, San Jose, Calif, USA, 2010.
  • K. D. Bowers, A. Juels, and A. Oprea, “Proofs of retrievability: theory and implementation,” in Proceedings of the ACM Workshop on Cloud Computing Security with the 16th ACM Computer and Communications Security Conference (CCS '09), R. Sion, Ed., pp. 43–54, ACM, New York, NY, USA, 2009.
  • R. Hamadi, H. Y. Paik, and B. Benatallah, “Conceptual modeling of privacy-aware web service protocols,” in Proceedings of the 19th International Conference on Advanced Information System Engineering (CAiSE '07), pp. 233–248, 2007.
  • N. Guermouche, S. Benbernou, E. Coquery, and M.-S. Hacid, “Privacy-aware web service protocol replaceability,” in Proceedings of the IEEE International Conference on Web Services (ICWS '07), pp. 1048–1055, 2007.
  • K. Mokhtari, S. Benbernou, M. Hacid, E. Coquery, F. Leymann, and M. Said, “Verification of privacy timed properties in web service protocols,” in Proceedings of the IEEE International Conference on Services Computing (SCC '08), pp. 593–594, 2008.
  • L. LinYuan, Z. Haibin, H. Zhiqiu, and X. Dongqing, “Minimal privacy authorization in web services collaboration,” Computer Standards & Interfaces, vol. 33, no. 3, pp. 332–343, 2011.
  • A. Barth, J. C. Mitchell, A. Datta, and S. Sundaram, “Privacy and utility in business processes,” in Proceedings of the 20th IEEE Computer Security Foundations Symposium (CSFS '07), pp. 279–294, 2007.
  • Z. Wei, M. Kang, D.-N. Jia, B. Yin, and W. Zhou, “Research on privacy-protection policy for pervasive computing,” Chinese Journal of Computers, vol. 33, no. 1, pp. 128–138, 2010.
  • H. Zhu and M. C. Zhou, “Role-based collaboration and its kernel mechanisms,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 36, no. 4, pp. 578–589, 2006.
  • K. El-Khatib, “A privacy negotiation protocol for web services,” in Workshop on Collaboration Agents: Autonomous Agents for Collaborative Environments, pp. 85–92, Halifax, 2003.
  • Y. Zhang and D.-G. Fen, “Parsimonious semantic trust negotiation,” Chinese Journal of Computers, vol. 32, no. 10, pp. 1989–2003, 2009.
  • J. Kolter, M. Netter, and G. Pernul, “Visualizing past personal data disclosures,” in Proceedings of the International Conference on Availability, Reliability, and Security (ARES '10), pp. 131–139, 2010.
  • L. LinYuan, Z. Haibin, and H. Zhiqiu, “Analysis of the minimal privacy disclosure for web services collaborations with role mechanisms,” Expert Systems with Applications, vol. 38, no. 4, pp. 4540–4549, 2011.
  • T. Yu, Y. Zhang, and K. J. Lin, “Modeling and measuring privacy risks in QoS web services,” in Proceedings of the 8th IEEE International Conference on E-Commerce Technology, 3th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE '06), p. 4, 2006.
  • J. I. Hong, J. D. Ng, S. Lederer, and J. A. Landay, “Privacy risk models for designing privacy-sensitive ubiquitous computing systems,” in Proceedings of the 5th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 91–100, 2004.
  • N. Weiwei and C. Zhihong, “Clustering-oriented privacy-preserving data publishing,” Knowledge-Based Systems, vol. 35, pp. 264–270, 2012.
  • D. E. Bakken, R. Parameswaran, D. M. Blough, A. A. Franz, and T. Y. J. Palmer, “Data obfuscation: anonymity and desensitlzation of usable data sets,” IEEE Security & Privacy, vol. 2, no. 6, pp. 34–41, 2004.
  • F. Bao, R. Deng, and P. Feng, “An efficient and practical scheme for privacy protection in the e-commerce of digital goods,” in Information Security and Cryptology–-ICISC 2000, pp. 162–170, 2001.
  • B. Gilburd, A. Schuster, and R. Wolff, “k-TTP: a new privacy model for large-scale distributed environments,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 563–568, ACM, 2004.
  • M. Ye, X. Wu, X. Hu, and D. Hua, “Anonymizing classification data using rough set theory,” Knowledge-Based Systems, vol. 43, pp. 82–94, 2013.
  • L. Sweeney, “Achieving k-anonymity privacy protection using generalization and suppression,” International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 10, no. 5, pp. 571–588, 2002. \endinput
    Mathematical Reviews (MathSciNet): MR1948200
    Digital Object Identifier: doi:10.1142/S021848850200165X

Hindawi

Journal of Applied Mathematics

New content alerts

Email RSS ToC RSS Article
  • You have access to this content.
  • You have partial access to this content.
  • You do not have access to this content.
Turn Off MathJax

What is MathJax?

More like this

+ See more