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Found 3 papers, 1 papers with code
Provable Privacy with Non-Private Pre-Processing
When analysing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting.
PILLAR: How to make semi-private learning more effective
In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data.
How unfair is private learning ?
As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair.
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