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Found 9 papers, 3 papers with code
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
Put all together, our evaluation shows that this novel DP-SGD analysis allows us to now formally show that DP-SGD leaks significantly less privacy for many datapoints (when trained on common benchmarks) than the current data-independent guarantee.
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another.
Sentence-level Privacy for Document Embeddings
User language data can contain highly sensitive personal content.
Privacy Amplification by Subsampling in Time Domain
When this is the case, we observe that the influence of a single participant (sensitivity) can be reduced by subsampling and/or filtering in time, while still meeting privacy requirements.
Privacy Implications of Shuffling
\ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the \textit{order} of the data.
A Shuffling Framework for Local Differential Privacy
ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the data.
Location Trace Privacy Under Conditional Priors
Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time.
A Non-Parametric Test to Detect Data-Copying in Generative Models
Detecting overfitting in generative models is an important challenge in machine learning.
Location Trace Privacy Under Conditional Priors
Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time.
