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MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data

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Abstract

This paper presents a novel ‘MapReduce Based Optimum Victim Item and Transaction Selection Approach (MR-OVnTSA)’ that provides a feasible and intelligent solution for protecting sensitive frequent itemsets present in big data. The approach advocates to resolve the captious challenges, existing knowledge hiding algorithms are encountering. The proposed solution optimally minimizes the side effect of hiding process on non-sensitive information, and maintains a balance between knowledge and privacy as well as handles the exponential growth in data volume efficiently. The algorithm plugs the most optimum item and transaction as victim, by intelligently analyzing their coverage value i.e. it chooses one with maximal impact on sensitive knowledge but minimal on non-sensitive information. Further, the MapReduce version of the proposed scheme resolves the issue of non-feasibility by processing large-scale data (big data) in a parallel fashion. Experiments have been demonstrated over real and synthetically generated large-scale datasets. Results evince that the proffered scheme is much more efficient and maintains the balance between the privacy preservation, data quality maintenance, and CPU time, when dealing with large voluminous big datasets compared to existing knowledge hiding techniques.

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Authors and Affiliations

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

    Shivani Sharma & Durga Toshniwal

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  1. Shivani Sharma
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  2. Durga Toshniwal
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Correspondence to Shivani Sharma.

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Appendix: Comparing proposed OVnTSA with hybrid scheme

Appendix: Comparing proposed OVnTSA with hybrid scheme

The presented OVnTSA approach is heuristics based scheme used for hiding sensitive patterns however, recently some recent approaches have been investigated using different methods like hybrid schemes [43].

Hybrid approach use border based scheme for hiding sensitive rules whereas proposed OVnTSA approach is sensitive frequent pattern hiding scheme. Therefore for comparing the performance in terms of both techniques, A set of frequent itemsets and all the derived rules out from those itemsets are considered as confidential. OVnTSA needs to hide the sensitive patterns and Hybrid needs to hide all the rules derived from those sensitive patterns. The performance is compared in terms of misses cost i.e. a number of non-sensitive itemsets lost over two different real benchmark datasets. From Fig. 6a and b, it can be clearly observed that with a different number of sensitive itemsets the misses cost using OVnTSA scheme is less than the hybrid scheme. Further, Fig. 6c and d, present the plot between misses cost verses varying minimum threshold. The experiments are performed over four benchmark datasets. It can be clearly stated that the proposed scheme has lower misses cost than the hybrid approach.

Fig. 6
figure 6

Comparative analysis of OVnTSA and hybrid approach in terms of total nonsensitive items lost during sanitization

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As the compared techniques are meant for a different purpose, i.e. Hybrid for rule hiding and OVnTSA is for sensitive pattern hiding, it may be possible they may perform differently in a real scenario. A hybrid approach may perform better for other applications i.e. when some set of sensitive rules is provided as input for sanitization. As it is possible that some of the rules which are derived from a sensitive itemset (in our case) maybe, are not sensitive or valid at all (i.e. do not hold the confidence value above the threshold).

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Sharma, S., Toshniwal, D. MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data. Appl Intell 50, 4241–4260 (2020). https://doi.org/10.1007/s10489-020-01749-6

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