Skip to main content
SpringerLink
Log in

A Privacy Protection Evaluation Mechanism for Dynamic Data Based on Chunk-Confusion

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Nowadays big data security plays a major issue in cloud computing. Chunk-confusion-based privacy protection mechanism (CCPPM) protects the privacy of the tenants in plaintext. But both multi-tenant applications’ data and tenants’ privacy requirements are dynamically changing, which will have a great effect on the underlying storage model of cloud data. Moreover, the tenants’ business processing will change the data distribution and destroy the distribution balance of privacy data, which makes the data stored in the cloud face the risk of leakage of privacy. Therefore, the paper proposed a privacy protection evaluation mechanism for dynamic data based on CCPPM. The paper firstly introduces three kinds of the privacy leakages due to unbalanced data under the CCPPM, and analyzes two methods used for attacking. Aiming at the privacy leakages and the attack methods, we proposed a dynamic data processing algorithm to record the tenants’ operation sequence and set up the corresponding evaluation formula. Next, we evaluated the effect of privacy protection from two aspects of simple attack and background-knowledge-based attack, and used the data distribution similarity privacy preserving dynamic evaluation algorithm presented in this paper to obtain the measurement results of privacy leakages. Finally, according to the evaluation results, the defense strategies are given to prevent data privacy leakages. The experimental evaluation proves that rationality of dynamic the evaluation mechanism proposed in this paper has better feasibility and practicality for big data privacy protection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Sweeney, L. (2002). k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 10, 557–570.

    Article  MathSciNet  MATH  Google Scholar 

  2. Tian, X. X., Wang, X. L., & Gao, M. (2010). Database as a Services-Security and privacy preserving. Chinese Journal of Software, 21(5), 991–1006.

    Article  Google Scholar 

  3. Shyam, N. K. (2015). DecenCrypto cloud: decentralized cryptography technique for secure communication over the clouds. Journal of Computer Sciences and Applications, 3(3), 73–78.

    Google Scholar 

  4. Yang, J. J., Li, J. Q., & Niu, Y. (2015). A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Generation Computer Systems, 43–44, 74–86.

    Article  Google Scholar 

  5. Denning, D. E., Akl, S. G., Heckman, M., et al. (1987). Views for multilevel database security. IEEE Transactions on Software Engineering, SE-1(2), 129–140.

    Article  Google Scholar 

  6. Karabulut, Y., & Nassi, I. (2009). Secure enterprise services consumption for SaaS technology platforms. Proceedings - International Conference on Data Engineering (pp. 1749–1756).

  7. Xiong, J. B., Li, F. H., Ma, J. F., Liu, X. M., Yao, Z. Q., & Chen, P. S. (2015). A full lifecycle privacy protection scheme for sensitive data in cloud computing. Peer-to-Peer Networking and Applications, 8(6), 1025–1037.

    Article  Google Scholar 

  8. Feng, D. G., & Li, M. Z, H. (2014). Big data security and privacy protection. Chinese Journal of Computers, 37(1), 246–258.

  9. Li, L., Li, Q. Z., Shi, Y. L., & Zhang, K. (2012). SAPS-A Single Attribute Protection Scheme for SaaS. Information, 15(1), 275–282.

    MathSciNet  MATH  Google Scholar 

  10. Zhang, K., Li, Q. Z., & Shi, Y. L. (2010). Research on data combination privacy preservation mechanism for SaaS. Chinese Journal of Computers, 33(11), 2044–2054.

    Article  Google Scholar 

  11. Shi, Y. L., Jiang, Z., & Zhang, K. (2013). Policy-Based Customized Privacy Preserving Mechanism for SaaS Applications. Grid and Pervasive Computing, 7861, 491–500.

    Article  Google Scholar 

  12. Shao, Y. L., & Shi, Y. L. (2014). A Novel Cloud Data Fragmentation Cluster-Based Privacy Preserving Mechanism IJGDC. International Journal of Grid and Distributed Computing,, 7(4), 21–32.

    Article  Google Scholar 

  13. Fan, C. I., & Huang, S. Y. (2013). Controllable privacy preserving search based on symmetric predicate encryption in cloud storage. Future Generation Computer Systems, 29, 1716–1724.

    Article  Google Scholar 

  14. Byun, J. W., Sohn, Y., Bertino, E., & Li, N. (2006). Secure Anonymization for Incremental Datasets. 3rd VLDB Workshop on Secure Data Management, 4165LNCS, 48–63.

  15. Shen, Y., Cui, W. J., Li, Q. Z., & Shi, Y. L. (2011). Hybrid fragmentation to preserve data privacy for SaaS. Proceedings-8th Web Information Systems and Applications Conference,WISA 2011,Workshop on Semantic Web and Ontology, SWON 2011 Workshop on Electronic Government Technology and Application, EGTA 2011 (pp.3–6).

  16. Saleh, E., Takouna, l., & Meinal, C. (2013). SignedQuery: Protecting users data in multi-tenant SaaS environments. Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics (pp.213–218).

  17. Patel, K., Singh, N., Parikh, K., Kumar K. S., & Sendhil, J. N. (2014). Data security and privacy using data partition and centric key management in cloud. 2014 International Conference on Information Communication and Embedded Systems.

  18. Tsai, W., Bai, X. Y., & Huang, Y. (2014). Software-as-a-service (SaaS): perspectives and challenges. SCIENCE CHINA Information Sciences, 57(5), 1–15.

    Article  Google Scholar 

  19. Zhou, S. G., Li, F., Tao, Y. F., & Xiao, X. K. (2009). Privacy Preservation in Database Applications: A Survey. Chinese Journal of Computers, 32(5), 847–863.

    Article  Google Scholar 

  20. Kailkhura, B., Brahma, S., Han, Y. S., & Varshney, P. K. (2013). Optimal distributed detection in the presence of Byzantines. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing –Proceedings (pp. 2925–2929).

  21. Li, Y. B., Dai, W. Y., Ming, Z., & Qiu, M. K. (2016). Privacy protection for preventing data over-collection in Smart City. IEEE Transactions on Computers, 65(5), 1339–1350.

    Article  MathSciNet  Google Scholar 

  22. Nadendla, V. S. S., Chen, H., & Varshney, P. K. (2010). Secure distributed detection in the presence of eavesdroppers. Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on (pp. 1437–1441).

  23. Mao, H., Shuai, X., & Kapadia, A. (2011). Loose Tweets: An Analysis of Privacy Leaks on Twitter. Proceedings of the 10th annual ACM workshop on Privacy in the electronic society, (pp. 1–12).

  24. Li, Z., & Oechtering T. J. (2014). Tandem distributed Bayesian detection withprivacy constraints. Acoustics, Speech and Signal Processing (ICASSP) (pp. 8168- 8172).

  25. Wang, C. M., Guo, Y. J., & Guo, Y. H. (2012). Privacy metric for user’s trajectory in location-based services. Journal of Software, 23, 352–360.

    Article  Google Scholar 

  26. Du, W., Teng, Z., & Zhu, Z. (2008). Privacy-maxent: integrating background knowledge in privacy quantification. Special Interest Group on Management of Data (ACM) (pp. 459–472).

  27. Liu, J. Q. (2012). Publishing set-valued data against realistic adversaries. Journal of Computer Science and Technology, 27, 24–36.

    Article  MATH  Google Scholar 

  28. Li, J. Y., Qiu, M. K., Ming, Z., Quan, G., Qin, X., & Gu, Z. H. (2012). Online optimization for scheduling Preemptable tasks on IaaS cloud systems. Journal of Parallel and Distributed Computing (JPDC), 72(5), 666–677.

    Article  Google Scholar 

  29. Qiu, M. Y., & Sha, E. (2009). Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Transactions on Design Automation of Electronic Systems (TODAES), 14(2), 1–30.

    Article  Google Scholar 

  30. Charikar, M. (2002). Similarity Estimation Techniques from Rounding Algorithms. Proceedings of the 34th Annual ACM Symposium on Theory of Computing (pp. 380–388).

  31. Xiong, W. P., & Li, D. (2013). Quality model for evaluating SaaS service. Proceedings of 4th International Conference on Emerging Intelligent Data and Web Technologies (pp. 83–87).

  32. Souza, Rafael, T. D., & Sergio, D. Z. (2015). Privacy-Preserving Mechanism for Monitoring Sensitive Data. Proceedings of 12th International Conference on Information Technology: New Generations (pp. 191–196).

  33. Sandha, Ganaga, M. D. (2014). Study on data security mechanism in cloud computing. Proceedings of 2014 2nd International Conference on Current Trends in Engineering and Technology (pp. 13–17).

Download references

Acknowledgments

The research work was supported by the National Natural Science Foundation of China under Grant No.61572295, 61272241, the Innovation Methods Work Special Project No.2015IM010200, the TaiShan Industrial Experts Programme of Shandong Province, the Natural Science Foundation of Shandong Province under Grant No.ZR2014FM031, ZR2013FQ014, the Shandong Province Science and Technology Major Special Project No.2015ZDJQ01002, 2015ZDXX0201B03, 2015ZDXX0201A04, the Shandong Province Key Research and Development Plan No.2015GGX101015, the Fundamental Research Funds of Shandong University No.2015JC031.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Yu-liang Shi, Yu Chen, Zhong-min Zhou & Li-zhen Cui

Authors
  1. Yu-liang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong-min Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li-zhen Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-liang Shi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Yl., Chen, Y., Zhou, Zm. et al. A Privacy Protection Evaluation Mechanism for Dynamic Data Based on Chunk-Confusion. J Sign Process Syst 89, 27–39 (2017). https://doi.org/10.1007/s11265-016-1161-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-016-1161-2

Keywords

Search

Navigation