Hiding sensitive itemsets with multiple objective optimization

  • Jerry Chun-Wei LinEmail author
  • Yuyu Zhang
  • Binbin Zhang
  • Philippe Fournier-Viger
  • Youcef Djenouri
Methodologies and Application


Privacy-preserving data mining (PPDM) has become an important research topic, as it can hide sensitive information, while ensuring that information can still be extracted for decision making. While performing the sanitization progress for hiding the sensitive information, three side effects such as hiding failure, missing cost, and artificial cost happen at the same time. Several evolutionary algorithms were introduced to minimize those three side effects of PPDM using a single-objective function that generates one solution for sanitization. This paper presents a multiobjective algorithm (NSGA2DT) with two strategies for hiding sensitive information with transaction deletion based on the NSGA-II framework. To obtain better balance of side effects, the designed NSGA2DT takes database dissimilarity (Dis) as one more factor to achieve better performance in terms of four side effects. Moreover, instead of a single solution of the sanitization progress, the designed NSGA2DT provides more than one solutions than those of single-objective evolutionary algorithms, which shows flexibility to select the most appropriate transactions for deletion depending on user’s preference. A Fast SoRting strategy (FSR) and the pre-large concept are utilized, respectively, in this paper to find the optimized transactions for deletion and speed up the iterative process. Based on the developed NSGA2DT, the set of several Pareto solutions can be easily discovered, thus avoiding the problem of local optimization of single-objective approaches. Besides, the designed NSGA2DT does not require to set initial weights for evaluating the side effects, and thus, the results could not be seriously influenced by the predefined weights. Experimental results show that the proposed NSGA2DT provides satisfactory results with reduced side effects, compared to previous evolutionary approaches with single-objective function.


PPDM Sanitization Evolutionary computation Pre-large concept Pareto solutions 



This research was partially supported by the Shenzhen Technical Project under JCYJ20170307151733005 and KQJSCX20170726103424709.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest in this paper.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jerry Chun-Wei Lin
    • 1
    • 2
    Email author
  • Yuyu Zhang
    • 1
  • Binbin Zhang
    • 3
    • 4
  • Philippe Fournier-Viger
    • 5
  • Youcef Djenouri
    • 6
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.Department of Computing, Mathematics, and PhysicsWestern Norway University of Applied SciencesBergenNorway
  3. 3.Department of Biochemistry and Molecular BiologyShenzhen University Health Science CenterShenzhenChina
  4. 4.Center for Anti-Aging and Regenerative MedicineShenzhen University Health Science CenterShenzhenChina
  5. 5.School of Natural Sciences and HumanitiesHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  6. 6.IMADA, Southern Denmark UniversityOdenseDenmark

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