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Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.
How to Generate and Exchange Secrets (Extended Abstract)
Yao, A. C.-C · 1986
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How to play any mental game or A completeness theorem for protocols with honest majority
Goldreich, O., Micali, S., and Wigderson, A · 1987
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Digital hardware implementation of sigmoid function and its derivative for artificial neural networks
Faiedh, H., Gafsi, Z., and Besbes, K · 2001
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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The Foundations of Cryptography – Volume 2, Basic Applications
Goldreich, O · 2004
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Adverse impact and test validation: A practitioner’s guide to valid and defensible employment testing
Biddle, D · 2006
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Cogitas, Ergo Sum
Schreurs, W., Hildebrandt, M., Kindt, E., and Vanfleteren, M · 2008
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Blind justice
Bennett Capers, I · 2012
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Why unbiased computational processes can lead to discriminative decision procedures
Calders, T. and Žliobaitė, I · 2012
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Multiparty computation from somewhat homomorphic encryption
Damgård, I., Pastro, V., Smart, N. P., and Zakarias, S · 2012
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
Earlier work this paper cites.
An architecture for practical actively secure MPC with dishonest majority
Keller, M., Scholl, P., and Smart, N. P · 2013
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UCI machine learning repository, 2013
Lichman, M · 2013
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Insurance telematics: Opportunities and challenges with the smartphone solution
Handel, P., Skog, I., Wahlstrom, J., Bonawiede, F., Welch, R., Ohlsson, J., and Ohlsson, M · 2014
Cited alongside, same era.
Certifying and removing disparate impact
Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Cited alongside, same era.
Obliv-c: A language for extensible data-oblivious computation
Zahur, S. and Evans, D · 2015
Cited alongside, same era.
Obliv-C: A Language for Extensible Data-Oblivious Computation
Zahur, S. and Evans, D · 2015
Cited alongside, same era.
Machine bias: There is software used across the country to predict future criminals. and it is biased against blacks
Žliobaitė, I. and Custers, B · 2016
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Privacy-preserving PCA on horizontally-partitioned data
Al-Rubaie, M., Wu, P. Y., Chang, J. M., and Kung, S · 2017
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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
Gascón, A., Schoppmann, P., Balle, B., Raykova, M., Doerner, J., Zahur, S., and Evans, D · 2017
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NHS cyber attack: Everything you need to know about ’biggest ransomware’ offensive in history
Graham, C · 2017
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Faster secure multi-party computation of AES and DES using lookup tables
Keller, M., Orsini, E., Rotaru, D., Scholl, P., Soria-Vazquez, E., and Vivek, S · 2017
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Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Cited alongside, same era.
Big data’s disparate impact
Barocas, S. and Selbst, A. D · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Cited alongside, same era.
Accountable algorithms
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., and Yu, H · 2016
Cited alongside, same era.
How To Simulate It – A Tutorial on the Simulation Proof Technique
Lindell, Y · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., and Ristenpart, T · 2016
Cited alongside, same era.
Oblivious neural network predictions via minionn transformations
Liu, J., Juuti, M., Lu, Y., and Asokan, N · 2017
Later among the works it cites.
SecureML: A system for scalable privacy-preserving machine learning
Mohassel, P. and Zhang, Y · 2017
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Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Veale, M. and Binns, R · 2017
Later among the works it cites.
Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling
Veale, M. and Edwards, L · 2017
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Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning
Grgić-Hlača, N., Zafar, M. B., Gummadi, K. P., and Weller, A · 2018
Closest in time.
Gazelle: A Low Latency Framework for Secure Neural Network Inference
Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A · 2018
Closest in time.
Overdrive: Making SPDZ great again
Keller, M., Pastro, V., and Rotaru, D · 2018
Closest in time.