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Found 7 papers, 1 papers with code
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier.
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset.
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness.
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning
This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients.
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server.
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters.
CryptoCredit: Securely Training Fair Models
When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias.
