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A sanitization approach for big data with improved data utility

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Abstract

The process of collaborative data mining may sometimes expose the sensitive patterns present inside the data which may be undesirable to the data owner. Sensitive Pattern Hiding (SPH) is a subfield of data mining that addresses this problem. However, most of the existing approaches used for hiding sensitive patterns cause high side-effect on non-sensitive patterns which in-turn reduces the utility of the sanitized dataset. Furthermore, most of them are sequential in nature and are not able to cope with massive amounts of data and often results in high execution time. To resolve these identified challenges of utility and non-feasibility, two parallelized approaches have been proposed named PGVIR and PHCR based on spark parallel computing framework which modifies the data such that no sensitive patterns can be extracted while maintaining the utility of the sanitized dataset. Experiments performed using benchmark dataset shows that PGVIR scales better and PHCR causes fewer side-effects to the data compared to the existing techniques.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Uttrakhand, 247667, India

    Udit Sharma, Durga Toshniwal & Shivani Sharma

Authors
  1. Udit Sharma
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  2. Durga Toshniwal
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  3. Shivani Sharma
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Corresponding author

Correspondence to Shivani Sharma.

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Appendix: Comparison with existing parallel SPH techniques

Appendix: Comparison with existing parallel SPH techniques

Another set of the experiment has been performed to judge the performance of PGVIR and PHCR with respect to the parallel version of MaxFIA and SWA schemes proposed in [10, 11]. The first experiment has been set up with varying data sizes. Figure 12a plots the execution time taken by the sanitization process with varying data sizes. The MST is set to 20% and the total sensitive patterns need to be masked is 50. It can be clearly observed that due to both ways parallelization i.e. data parallelization and computing parallelization achieved in PGVIR and PHCR, the proposed scheme performs better than the existing state of art. Further, proposed schemes are implemented using the Spark platform and Parallel MaxFIA and SWA have been implemented using Hadoop MapReduce which again is the reason for the clear difference. Spark [26] platform is faster than the Hadoop MapReduce due to several reason like in-memory computation, data frame creation etc. Further the initilization time of hadoop is much higher than the Spark.

Second Set of experiment have been performed to analyze the performance in terms of running time with varying minimum support threshold value. Figure 12b presents the plot between running time and varying MST. It can be observed that with different minimum threshold value the execution time of PGVIR and PHCR is considerably less than the parallel MaxFIA and SWA. Therefore, it can be stated that due to both ways parallelization and use of the Spark platform make proposed PGVIR and PHCR a better choice for preserving the privacy of sensitive data.

Fig. 12
figure 12

Running Time of Parallel MaxFIA and SWA Vs Proposed PGVIR and PHCR

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Sharma, U., Toshniwal, D. & Sharma, S. A sanitization approach for big data with improved data utility. Appl Intell 50, 2025–2039 (2020). https://doi.org/10.1007/s10489-020-01640-4

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