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Found 6 papers, 1 papers with code
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Syfer: Neural Obfuscation for Private Data Release
We quantify privacy as the number of attacker guesses required to re-identify a single image (guesswork).
Rainbow Differential Privacy
We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice.
Generalized Rainbow Differential Privacy
In this framework, datasets are nodes in a graph, and two neighboring datasets are connected by an edge.
CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation
The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history.
