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On the Privacy of Decentralized Machine Learning



Dario Pasquini , Mathilde Raynal , Carmela Troncoso

* 17 pages 

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Synthetic Data -- A Privacy Mirage



Theresa Stadler , Bristena Oprisanu , Carmela Troncoso


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Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning



Mohammad Yaghini , Bogdan Kulynych , Carmela Troncoso

* Mohammad Yaghini and Bogdan Kulynych contributed equally to this work 

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Questioning the assumptions behind fairness solutions



Rebekah Overdorf , Bogdan Kulynych , Ero Balsa , Carmela Troncoso , Seda Gürses

* Presented at Critiquing and Correcting Trends in Machine Learning (NeurIPS 2018 Workshop), Montreal, Canada. This is a short version of arXiv:1806.02711 

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Evading classifiers in discrete domains with provable optimality guarantees



Bogdan Kulynych , Jamie Hayes , Nikita Samarin , Carmela Troncoso


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POTs: Protective Optimization Technologies



Rebekah Overdorf , Bogdan Kulynych , Ero Balsa , Carmela Troncoso , Seda Gürses

* An earlier version (v1/v2) by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at The Workshop on Hot Topics in Privacy Enhancing Technologies 2018 (HotPETs) and as a Poster at Privacy in Machine Learning and Artificial Intelligence, FAIM Workshop 2018 (PiMLAI) 

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Feature importance scores and lossless feature pruning using Banzhaf power indices



Bogdan Kulynych , Carmela Troncoso

* Presented at NIPS 2017 Symposium on Interpretable Machine Learning 

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