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Proceedings of the International Conference on Big Data, IoT, and Machine Learning pp 205–215Cite as

Enhancing the Performance of 3D Rotation Perturbation in Privacy Preserving Data Mining Using Correlation Based Feature Selection

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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 95)

Abstract

A large amount of valuable data is being produced every day with the development of technologies. To retrieve knowledge and information from these data, mining and analysis are mandatory. But, the data may contain sensitive information of the individuals like medical diagnostic reports which they do not want to expose. Privacy preserving data mining, i.e., PPDM can help in this issue keeping the sensitive information private as well as preserving the data utility. Rotation-based perturbation technique contributes to satisfying both aspects of PPDM, i.e., individuals’ privacy and data utility besides other PPDM techniques. In this work, we proposed a way for generating the triplet (set of three features) for 3D rotation perturbation technique using correlation among the features. This triplet generation is a fundamental step in 3D rotation perturbation technique. The analysis of information entropy, privacy protection and utility analysis elucidates that correlation-based triplet generation provides better data privacy and utility than existing triplet generation for 3D rotation perturbation technique.

Keywords

  • Privacy
  • Data utility
  • Correlation
  • Perturbation
  • Rotation

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  • DOI: 10.1007/978-981-16-6636-0_17
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References

  1. Salloum SA, Alshurideh M, Elnagar A, Shaalan K (2020) Mining in educational data: review and future directions. In: Advances in intelligent systems and computing. Springer International Publishing, Berlin, pp 92–102

    Google Scholar 

  2. Peña-Ayala A (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41:1432–1462

    Google Scholar 

  3. Afrin A, Paul MK, Sattar AHMS (2019) Privacy preserving data mining using non-negative matrix factorization and singular value decomposition. In: 2019 4th International conference on electrical information and communication technology (EICT). IEEE, pp 1–6

    Google Scholar 

  4. Chamikara MAP, Bertok P, Liu D, Camtepe S, Khalil I (2020) Efficient privacy preservation of big data for accurate data mining. Inf Sci 527:420–443

    Google Scholar 

  5. Agrawal R, Srikant R (2000) Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data—SIGMOD’00. ACM Press, pp 439–450

    Google Scholar 

  6. Chen K, Liu L (2010) Geometric data perturbation for privacy preserving outsourced data mining. Knowl Inf Syst 29:657–695

    Google Scholar 

  7. Oliveira S, Zaiane O (2004) Data perturbation by rotation for privacy-preserving clustering. University of Alberta Libraries

    Google Scholar 

  8. Upadhyay S, Sharma C, Sharma P, Bharadwaj P, Seeja KR (2018) Privacy preserving data mining with 3-D rotation transformation. J King Saud Univ Comput Inf Sci 30:524–530

    Google Scholar 

  9. Stanley RMO, Osmar RZ (2010) Privacy preserving clustering by data transformation. J Inf Data Manage 1:37

    Google Scholar 

  10. Denham B, Pears R, Naeem MA (2020) Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining. Expert Syst Appl 152:113380

    Google Scholar 

  11. Chamikara MAP, Bertok P, Liu D, Camtepe S, Khalil I (2018) Efficient data perturbation for privacy preserving and accurate data stream mining. Pervasive Mob Comput 48:1–19

    Google Scholar 

  12. Chamikara MAP, Bertok P, Liu D, Camtepe S, Khalil I (2019) An efficient and scalable privacy preserving algorithm for big data and data streams. Comput Secur 87:101570

    Google Scholar 

  13. Singh D, Singh B (2020) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97:105524

    Google Scholar 

  14. Xu S, Zhang J, Han D, Wang J (2006) Singular value decomposition based data distortion strategy for privacy protection. Knowl Inf Syst 10:383–397

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh

    Mahit Kumar Paul & Md. Rabiul Islam

Authors
  1. Mahit Kumar Paul
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  2. Md. Rabiul Islam
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Editor information

Editors and Affiliations

  1. Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh

    Dr. Mohammad Shamsul Arefin

  2. Jahangirnagar University, Dhaka, Bangladesh

    Dr. M. Shamim Kaiser

  3. National Institute for Materials Science, Tsukuba, Japan

    Dr. Anirban Bandyopadhyay

  4. University of Dhaka, Dhaka, Bangladesh

    Dr. Md. Atiqur Rahman Ahad

  5. Amity University, Jaipur, India

    Prof. Kanad Ray

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Paul, M.K., Islam, M.R. (2022). Enhancing the Performance of 3D Rotation Perturbation in Privacy Preserving Data Mining Using Correlation Based Feature Selection. In: Arefin, M.S., Kaiser, M.S., Bandyopadhyay, A., Ahad, M.A.R., Ray, K. (eds) Proceedings of the International Conference on Big Data, IoT, and Machine Learning. Lecture Notes on Data Engineering and Communications Technologies, vol 95. Springer, Singapore. https://doi.org/10.1007/978-981-16-6636-0_17

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  • DOI: https://doi.org/10.1007/978-981-16-6636-0_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6635-3

  • Online ISBN: 978-981-16-6636-0

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