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Nicholas Franzese

2 accepted papers

2023

Confidential-PROFITT: Confidential PROof of FaIr Training of Trees

ICLR 2023top-5%

Post hoc auditing of model fairness suffers from potential drawbacks: (1) auditing may be highly sensitive to the test samples chosen; (2) the model and/or its training data may need to be shared with an auditor thereby breaking confidentiality. We address these issues by instead providing a certifi…

Cited by 22SourcePDFScholar
2023

Robust and Actively Secure Serverless Collaborative Learning

NeurIPS 2023poster

Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively protect user data, they remain vulnerable to either the server, the clients, or both, deviating from the protocol. Indeed, be…

Cited by 4SourcePDFScholar