Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption-Reference-Cited by-同舟云学术

Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Park Saerom¹,Byun Junyoung²,Lee Joohee¹

Affiliation:

1. Sungshin Women's University, Republic of Korea

2. Seoul National University, Republic of Korea

Funder

National Research Foundation of Korea(NRF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512252

Reference50 articles.

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2. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias. ProPublica. See https://www. propublica. org/article/machine-bias-risk-assessments-in-criminal-sentencing(2016). Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias. ProPublica. See https://www. propublica. org/article/machine-bias-risk-assessments-in-criminal-sentencing(2016).

3. Anthony Barnett , Jay Santokhi , Michael Simpson , Nigel P Smart , Charlie Stainton-Bygrave, Srinivas Vivek, and Adrian Waller. 2017 . Image Classification using non-linear Support Vector Machines on Encrypted Data.IACR Cryptol. ePrint Arch . 2017 (2017), 857. Anthony Barnett, Jay Santokhi, Michael Simpson, Nigel P Smart, Charlie Stainton-Bygrave, Srinivas Vivek, and Adrian Waller. 2017. Image Classification using non-linear Support Vector Machines on Encrypted Data.IACR Cryptol. ePrint Arch. 2017 (2017), 857.

4. Adi Ben-Israel. 1965. An iterative method for computing the generalized inverse of an arbitrary matrix. Math. Comp. (1965) 452–455. Adi Ben-Israel. 1965. An iterative method for computing the generalized inverse of an arbitrary matrix. Math. Comp. (1965) 452–455.

5. Alex Beutel Jilin Chen Zhe Zhao and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075(2017). Alex Beutel Jilin Chen Zhe Zhao and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075(2017).

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