Skip to main content
SpringerLink
Log in

MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation

  • Special Issue Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Database fragmentation has been used as a protection mechanism of database’s privacy by allocating attributes with sensitive associations into separate data fragments. A typical relational database consists of multiple relations. Thus, fragmentation process is applied to each relation separately in a sequential manner. In other words, the existing database fragmentation approaches regard each relation fragmentation problem as an independent task. When solving a sequence of fragmentation problems, redundant computational resources are consumed when extracting the same fragmentation information and limit the performance of those algorithms. In this paper, a multitasking database fragmentation problem for privacy preservation requirements is formally defined. A multitasking distributed differential evolution algorithm is introduced, including a multitasking distributed framework enriched by two new operators. The introduced framework can help exchange generic and effective allocation information among different database fragmentation problems. A similarity-based alignment operator is proposed to adjust the fragment orders in different database fragmentation solutions. A perturbation-based mutation operator with adaptive mutation strategy selection is designed to sufficiently exchange evolutionary information in the solutions. Experimental results show that the proposed algorithm can outperform other competitors in terms of solution accuracy, convergence speed, and scalability.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. https://health.data.ny.gov/Health/Hospital-Inpatient-Discharges-SPARCS-De-Identified/82xm-y6g8.

References

  1. Aggarwal, G., Bawa, M., Ganesan, P., Garcia-Molina, H., Kenthapadi, K., Motwani, R., Srivastava, U., Thomas, D., Xu, Y.: Two can keep a secret: a distributed architecture for secure database services. In: 2005 CIDR Conference (2005)

  2. Attasena, V., Darmont, J., Harbi, N.: Secret sharing for cloud data security: a survey. VLDB J. 26(5), 657–681 (2017). https://doi.org/10.1007/s00778-017-0470-9

    Article  Google Scholar 

  3. Ciriani, V., Di Vimercati, S.D.C., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Fragmentation and encryption to enforce privacy in data storage. In: European Symposium on Research in Computer Security, pp. 171–186. Springer (2007)

  4. Ciriani, V., Vimercati, S.D.C.D., Foresti, S., Jajodia, S., Paraboschi, S., Samarati, P.: Combining fragmentation and encryption to protect privacy in data storage. ACM Trans. Inf. Syst. Secur. 13(3), 1–33 (2010)

    Article  Google Scholar 

  5. De Capitani di Vimercati, S., Foresti, S., Jajodia, S., Livraga, G., Paraboschi, S., Samarati, P.: Loose associations to increase utility in data publishing. J. Comput. Secur. 23(1), 59–88 (2015)

  6. Feng, L., Huang, Y., Zhou, L., Zhong, J., Gupta, A., Tang, K., Tan, K.C.: Explicit evolutionary multitasking for combinatorial optimization: a case study on capacitated vehicle routing problem. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/tcyb.2019.2962865

    Article  Google Scholar 

  7. Gao, J., Yu, J.X., Jin, R., Zhou, J., Wang, T., Yang, D.: Outsourcing shortest distance computing with privacy protection. VLDB J. 22(4), 543–559 (2013). https://doi.org/10.1007/s00778-012-0304-8

    Article  Google Scholar 

  8. Gao, Z., Pan, Z., Zuo, C., Gao, J., Xu, Z.: An optimized deep network representation of multimutation differential evolution and its application in seismic inversion. IEEE Trans. Geosci. Remote Sens. 57(7), 4720–4734 (2019). https://doi.org/10.1109/tgrs.2019.2892567

    Article  Google Scholar 

  9. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)

    MATH  Google Scholar 

  10. Ge, Y.F., Yu, W.J., Lin, Y., Gong, Y.J., Zhan, Z.H., Chen, W.N., Zhang, J.: Distributed differential evolution based on adaptive mergence and split for large-scale optimization. IEEE Trans. Cybern. 48(7), 2166–2180 (2018). https://doi.org/10.1109/tcyb.2017.2728725

    Article  Google Scholar 

  11. Ge, Y.F., Yu, W.J., Zhan, Z.H., Zhang, J.: Competition-based distributed differential evolution. In: 2018 IEEE Congress on Evolutionary Computation. IEEE (2018). https://doi.org/10.1109/cec.2018.8477758

  12. Ge, Y.F., Cao, J., Wang, H., Zhang, Y., Chen, Z.: Distributed differential evolution for anonymity-driven vertical fragmentation in outsourced data storage. In: 2020 International Conference on Web Information Systems Engineering, pp. 213–226 (2020)

  13. Ge, Y.F., Yu, W.J., Cao, J., Wang, H., Zhan, Z.H., Zhang, Y., Zhang, J.: Distributed memetic algorithm for outsourced database fragmentation. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/tcyb.2020.3027962

    Article  Google Scholar 

  14. Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016). https://doi.org/10.1109/tevc.2015.2458037

    Article  Google Scholar 

  15. Hore, B., Mehrotra, S., Canim, M., Kantarcioglu, M.: Secure multidimensional range queries over outsourced data. VLDB J. 21(3), 333–358 (2011). https://doi.org/10.1007/s00778-011-0245-7

    Article  Google Scholar 

  16. Köhler, J., Jünemann, K., Hartenstein, H.: Confidential database-as-a-service approaches: taxonomy and survey. J. Cloud Comput. 4(1), 1–14 (2015)

    Article  Google Scholar 

  17. Li, J., Yao, W., Zhang, Y., Qian, H., Han, J.: Flexible and fine-grained attribute-based data storage in cloud computing. IEEE Trans. Serv. Comput. 10(5), 785–796 (2017). https://doi.org/10.1109/tsc.2016.2520932

    Article  Google Scholar 

  18. Liaw, R.T., Ting, C.K.: Evolutionary many-tasking based on biocoenosis through symbiosis: a framework and benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation, pp. 2266–2273. IEEE (2017). https://doi.org/10.1109/cec.2017.7969579

  19. Liaw, R.T., Ting, C.K.: Evolutionary manytasking optimization based on symbiosis in biocoenosis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4295–4303 (2019). https://doi.org/10.1609/aaai.v33i01.33014295

  20. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2006)

    MATH  Google Scholar 

  21. Price, K.V.: Differential evolution. In: Handbook of Optimization, pp. 187–214 (2013)

  22. Rani, K., Sagar, R.K.: Enhanced data storage security in cloud environment using encryption, compression and splitting technique. In: 2017 International Conference on Telecommunication and Networks. IEEE (2017) https://doi.org/10.1109/tel-net.2017.8343557

  23. UbaidurRahman, N.H., Balamurugan, C., Mariappan, R.: A novel DNA computing based encryption and decryption algorithm. Proc. Comput. Sci. 46, 463–475 (2015)

    Article  Google Scholar 

  24. Wang, Y., Yan, Z., Feng, W., Liu, S.: Privacy protection in mobile crowd sensing: a survey. World Wide Web 23(1), 421–452 (2019). https://doi.org/10.1007/s11280-019-00745-2

    Article  Google Scholar 

  25. Xu, X., Xiong, L., Liu, J.: Database fragmentation with confidentiality constraints: a graph search approach. In: 2015 ACM Conference on Data and Application Security and Privacy, pp. 263–270 (2015)

  26. Yu, J., Wang, G., Mu, Y., Gao, W.: An efficient generic framework for three-factor authentication with provably secure instantiation. IEEE Trans. Inf. Forensics Secur. 9(12), 2302–2313 (2014)

    Article  Google Scholar 

  27. Yu, Y., Au, M.H., Ateniese, G., Huang, X., Susilo, W., Dai, Y., Min, G.: Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Trans. Inf. Forensics Secur. 12(4), 767–778 (2017)

    Article  Google Scholar 

  28. Zheng, L.M., Zhang, S.X., Zheng, S.Y., Pan, Y.M.: Differential evolution algorithm with two-step subpopulation strategy and its application in microwave circuit designs. IEEE Trans. Ind. Inform. 12(3), 911–923 (2016). https://doi.org/10.1109/tii.2016.2535347

    Article  Google Scholar 

  29. Zhou, X.G., Peng, C.X., Liu, J., Zhang, Y., Zhang, G.J.: Underestimation-assisted global-local cooperative differential evolution and the application to protein structure prediction. IEEE Trans. Evol. Comput. 24(3), 536–550 (2019). https://doi.org/10.1109/tevc.2019.2938531

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia

    Yong-Feng Ge & Jinli Cao

  2. Faculty of Information Technology, Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Maria Orlowska

  3. Institute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, Australia

    Hua Wang & Yanchun Zhang

Authors
  1. Yong-Feng Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Orlowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinli Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanchun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, YF., Orlowska, M., Cao, J. et al. MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation. The VLDB Journal 31, 957–975 (2022). https://doi.org/10.1007/s00778-021-00718-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-021-00718-w

Keywords

Search

Navigation