The paper introduces a framework for collusion between train- and inference-time adversaries in ML pipelines, proposes a guideline for conjecturing collusion potential, explains prior work, and empirically validates five cases.
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, and Mohamed Siala
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Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
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SoK: Colluding Adversaries in Machine Learning Pipelines
The paper introduces a framework for collusion between train- and inference-time adversaries in ML pipelines, proposes a guideline for conjecturing collusion potential, explains prior work, and empirically validates five cases.
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Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.