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Found 7 papers, 1 papers with code
Privacy-Preserving Algorithmic Recourse
We observe that PrivRecourse can provide paths that are private and realistic.
Privacy-Preserving Fair Item Ranking
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis.
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education.
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance.
Training Differentially Private Models with Secure Multiparty Computation
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data.
Privacy-Preserving Video Classification with Convolutional Neural Networks
We propose a privacy-preserving implementation of single-frame method based video classification with convolutional neural networks that allows a party to infer a label from a video without necessitating the video owner to disclose their video to other entities in an unencrypted manner.
