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Found 6 papers, 1 papers with code
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.
Syfer: Neural Obfuscation for Private Data Release
We quantify privacy as the number of attacker guesses required to re-identify a single image (guesswork).
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
Noise Recycling
The estimate is recycled to reduce the Signal to Noise Ratio (SNR) of an orthogonal channel that is experiencing correlated noise and so improve the accuracy of its decoding.
Information Theory Information Theory
Inferring differentiation order in adaptive immune responses from population level data
An alternative possibility, supported by the data in Kinjo et al. (Nature Commun., 2015), is that memory precursors are created after the expansion phase, which is a deduction not possible from the mathematical methods provided in Buchholz et al. (Science, 2013).
Privacy with Estimation Guarantees
Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy).
