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Attacking Machine Learning Models for Social Good

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Decision and Game Theory for Security (GameSec 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12513))

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Abstract

As machine learning (ML) techniques are becoming widely used, awareness of the harmful effect of automation is growing. Especially, in problem domains where critical decisions are made, machine learning-based applications may raise ethical issues with respect to fairness and privacy. Existing research on fairness and privacy in the ML community mainly focuses on providing remedies during the ML model training phase. Unfortunately, such remedies may not be voluntarily adopted by the industry that is concerned about the profits. In this paper, we propose to apply, from the user’s end, a fair and legitimate technique to “game” the ML system to ameliorate its social accountability issues. We show that although adversarial attacks can be exploited to tamper with ML systems, they can also be used for social good. We demonstrate the effectiveness of our proposed technique on real world image and credit data.

The research reported herein was supported in part by NIH award 1R01HG006844, NSF awards CNS-1837627, OAC-1828467, IIS-1939728 and ARO award W911NF-17-1-0356.

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Notes

  1. 1.

    Although the gender information is not privacy sensitive, we use this as a substitute for more privacy-sensitive concept such as sexual orientation.

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Author information

Authors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, 75080, USA

    Vibha Belavadi, Yan Zhou, Murat Kantarcioglu & Bhavani Thuriasingham

Authors
  1. Vibha Belavadi
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  2. Yan Zhou
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  3. Murat Kantarcioglu
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  4. Bhavani Thuriasingham
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Corresponding author

Correspondence to Murat Kantarcioglu .

Editor information

Editors and Affiliations

  1. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Quanyan Zhu

  2. ISR, University of Maryland, College Park, MD, USA

    John S. Baras

  3. Electrical Engineering, University of Washington, Seattle, WA, USA

    Radha Poovendran

  4. New York University, New York, NY, USA

    Juntao Chen

Appendix A

Appendix A

We consider the following attributes to change in our German Credit data:

  1. 1.

    Purpose: For getting the loan ex. car(new), car(old), repairs, education, etc.

  2. 2.

    Duration: Increase/decrease the duration (in months) to see the change in granting loan.

  3. 3.

    Credit amount: Increase and decrease the credit amount granted as a matter of percentage of original amount. ex: 1.05x, 1.10x, 0.90x, 0.85x where x is the current amount.

  4. 4.

    Savings account/bonds: Change the number of savings and bonds from None (A65) to ‘...100 DM’ (A61).

  5. 5.

    Other installment plans: Change from None (A143) to Bank/Store (A141/A142).

  6. 6.

    Telephone: Change the ownership of telephone from None (A191) to registered in user’s name (A192).

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Belavadi, V., Zhou, Y., Kantarcioglu, M., Thuriasingham, B. (2020). Attacking Machine Learning Models for Social Good. In: Zhu, Q., Baras, J.S., Poovendran, R., Chen, J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science(), vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_25

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  • DOI: https://doi.org/10.1007/978-3-030-64793-3_25

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