IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0127834.html
   My bibliography  Save this article

Use HypE to Hide Association Rules by Adding Items

Author

Listed:
  • Peng Cheng
  • Chun-Wei Lin
  • Jeng-Shyang Pan

Abstract

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases.

Suggested Citation

  • Peng Cheng & Chun-Wei Lin & Jeng-Shyang Pan, 2015. "Use HypE to Hide Association Rules by Adding Items," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-19, June.
  • Handle: RePEc:plo:pone00:0127834
    DOI: 10.1371/journal.pone.0127834
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127834
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0127834&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0127834?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Syam Menon & Sumit Sarkar, 2007. "Minimizing Information Loss and Preserving Privacy," Management Science, INFORMS, vol. 53(1), pages 101-116, January.
    3. Syam Menon & Sumit Sarkar & Shibnath Mukherjee, 2005. "Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns," Information Systems Research, INFORMS, vol. 16(3), pages 256-270, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Syam Menon & Abhijeet Ghoshal & Sumit Sarkar, 2022. "Modifying Transactional Databases to Hide Sensitive Association Rules," Information Systems Research, INFORMS, vol. 33(1), pages 152-178, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Syam Menon & Abhijeet Ghoshal & Sumit Sarkar, 2022. "Modifying Transactional Databases to Hide Sensitive Association Rules," Information Systems Research, INFORMS, vol. 33(1), pages 152-178, March.
    2. Abhijeet Ghoshal & Jing Hao & Syam Menon & Sumit Sarkar, 2020. "Hiding Sensitive Information when Sharing Distributed Transactional Data," Information Systems Research, INFORMS, vol. 31(2), pages 473-490, June.
    3. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    4. Gong, Wenyin & Cai, Zhihua, 2009. "An improved multiobjective differential evolution based on Pareto-adaptive [epsilon]-dominance and orthogonal design," European Journal of Operational Research, Elsevier, vol. 198(2), pages 576-601, October.
    5. Andrea Ponti & Antonio Candelieri & Ilaria Giordani & Francesco Archetti, 2023. "Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    6. Yunsong Han & Hong Yu & Cheng Sun, 2017. "Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions," Sustainability, MDPI, vol. 9(12), pages 1-18, December.
    7. Sergio Cabello, 2023. "Faster distance-based representative skyline and k-center along pareto front in the plane," Journal of Global Optimization, Springer, vol. 86(2), pages 441-466, June.
    8. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    9. Braun, Marlon & Shukla, Pradyumn, 2024. "On cone-based decompositions of proper Pareto-optimality in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 592-602.
    10. Jesús Martínez-Frutos & David Herrero-Pérez, 2016. "Kriging-based infill sampling criterion for constraint handling in multi-objective optimization," Journal of Global Optimization, Springer, vol. 64(1), pages 97-115, January.
    11. José Antonio Castán Rocha & Alejandro Santiago & Alejandro H. García-Ruiz & Jesús David Terán-Villanueva & Salvador Ibarra Martínez & Mayra Guadalupe Treviño Berrones, 2024. "Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching Off Completely Idle Machines," Mathematics, MDPI, vol. 12(23), pages 1-53, November.
    12. Dubois-Lacoste, Jérémie & López-Ibáñez, Manuel & Stützle, Thomas, 2015. "Anytime Pareto local search," European Journal of Operational Research, Elsevier, vol. 243(2), pages 369-385.
    13. Hyoungjin Kim & Meng-Sing Liou, 2013. "New fitness sharing approach for multi-objective genetic algorithms," Journal of Global Optimization, Springer, vol. 55(3), pages 579-595, March.
    14. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
    15. Miettinen, Kaisa & Molina, Julián & González, Mercedes & Hernández-Díaz, Alfredo & Caballero, Rafael, 2009. "Using box indices in supporting comparison in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 197(1), pages 17-24, August.
    16. Tangpattanakul, Panwadee & Jozefowiez, Nicolas & Lopez, Pierre, 2015. "A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite," European Journal of Operational Research, Elsevier, vol. 245(2), pages 542-554.
    17. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
    18. Cosson, Raphaël & Santana, Roberto & Derbel, Bilel & Liefooghe, Arnaud, 2024. "On bi-objective combinatorial optimization with heterogeneous objectives," European Journal of Operational Research, Elsevier, vol. 319(1), pages 89-101.
    19. Allmendinger, Richard & Handl, Julia & Knowles, Joshua, 2015. "Multiobjective optimization: When objectives exhibit non-uniform latencies," European Journal of Operational Research, Elsevier, vol. 243(2), pages 497-513.
    20. Weihua Qian & Hang Xu & Houjin Chen & Lvqing Yang & Yuanguo Lin & Rui Xu & Mulan Yang & Minghong Liao, 2024. "A Synergistic MOEA Algorithm with GANs for Complex Data Analysis," Mathematics, MDPI, vol. 12(2), pages 1-30, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0127834. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.