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Study on Data Anonymization for Deep Learning

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Recent Trends and Future Technology in Applied Intelligence (IEA/AIE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10868))

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Abstract

In this paper, we propose privacy protection data mining through deep learning. We discuss existing privacy protection data mining, study its features, and examine an anonymizing tool for deep learning. Experiments using anonymization tools (UAT) confirmed that deep learning does not reduce accuracy by making it anonymous.

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References

  1. Alphago – deepmind. Accessed 24 Feb 2018

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Privacy-preserving data mining. SIGMOD Rec. 29(2), 439–450 (2000)

    Article  Google Scholar 

  3. Aggarwal, C.C., Yu, P.S.: A general survey of privacy-preserving data mining models and algorithms. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining. Advances in Database Systems, vol. 34, pp. 11–52. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-70992-5_2

    Chapter  Google Scholar 

  4. Sakuma, J., Kobayashi, S.: Privacy-Preserving Data Mining. Jpn. Soc. Artif. Intell. 24(2), 283–294 (2009)

    Google Scholar 

  5. Cramer, R., Damgård, I., Nielsen, J.B.: Multiparty computation from threshold homomorphic encryption. In: Pfitzmann, B. (ed.) EUROCRYPT 2001. LNCS, vol. 2045, pp. 280–300. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44987-6_18

    Chapter  Google Scholar 

  6. Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. In: 22nd International Conference on Data Engineering (ICDE 2006), pp. 24–24, April 2006

    Google Scholar 

  7. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  8. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  Google Scholar 

  9. Le, Q.: Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8595–8598, May 2013

    Google Scholar 

  10. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout Networks. ArXiv e-prints, February 2013

    Google Scholar 

  11. Niimi, A.: Deep learning for credit card data analysis. In: World Congress on Internet Security (WorldCIS-2015), Dublin, Ireland, pp. 73–77, October 2015

    Google Scholar 

  12. Niimi, A.: Deep learning with large scale dataset for credit card data analysis. In: Fuzzy Systems and Data Mining II, Proceedings of FSDM 2016, Macau, pp. 149–158, December 2016

    Google Scholar 

  13. Apache Spark, lightning-fast cluster computing. Accessed 15 Sept 2015

    Google Scholar 

  14. 0xdata - H2O.ai - fast scalable machine learning. Accessed 15 Sept 2015

    Google Scholar 

  15. Candel, A., Parmar, V.: Deep Learning with H2O. H2O (2015). Accessed 15 Sept 2015

    Google Scholar 

  16. SparkR (R on Spark) - Spark 1.5.0 documentation. Accessed 15 Sept 2015

    Google Scholar 

  17. Cornell anonymization toolkit. Accessed 31 Jan 2017

    Google Scholar 

  18. UTD anonymization toolbox. Accessed 31 Jan 2017

    Google Scholar 

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Author information

Authors and Affiliations

  1. Future University Hakodate, 2-116 Kamedanakano, Hakodate, Hokkaido, 041-8655, Japan

    Ayahiko Niimi

Authors
  1. Ayahiko Niimi
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Corresponding author

Correspondence to Ayahiko Niimi .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Malek Mouhoub

  2. University of Regina, Regina, SK, Canada

    Samira Sadaoui

  3. Concordia University, Montreal, QC, Canada

    Otmane Ait Mohamed

  4. Texas State University, San Marcos, TX, USA

    Moonis Ali

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Niimi, A. (2018). Study on Data Anonymization for Deep Learning. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_74

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  • DOI: https://doi.org/10.1007/978-3-319-92058-0_74

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92057-3

  • Online ISBN: 978-3-319-92058-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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