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Found 9 papers, 4 papers with code
Harnessing large-language models to generate private synthetic text
An alternative approach, which this paper studies, is to use a sensitive dataset to generate synthetic data that is differentially private with respect to the original data, and then non-privately training a model on the synthetic data.
Poisoning Web-Scale Training Datasets is Practical
Deep learning models are often trained on distributed, webscale datasets crawled from the internet.
Tight Auditing of Differentially Private Machine Learning
Moreover, our auditing scheme requires only two training runs (instead of thousands) to produce tight privacy estimates, by adapting recent advances in tight composition theorems for differential privacy.
The Privacy Onion Effect: Memorization is Relative
Machine learning models trained on private datasets have been shown to leak their private data.
Debugging Differential Privacy: A Case Study for Privacy Auditing
Differential Privacy can provide provable privacy guarantees for training data in machine learning.
Toward Training at ImageNet Scale with Differential Privacy
Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy.
Membership Inference Attacks From First Principles
A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset.
Poisoning and Backdooring Contrastive Learning
Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets.
High Frequency Remote Monitoring of Parkinson's Disease via Smartphone: Platform Overview and Medication Response Detection
Objective: The aim of this study is to develop a smartphone-based high-frequency remote monitoring platform, assess its feasibility for remote monitoring of symptoms in Parkinson's disease, and demonstrate the value of data collected using the platform by detecting dopaminergic medication response.
Computers and Society
