5 papers
Differentially Private Synthetic Data Release for Topics API Outputs
Travis Dick, Alessandro Epasto, Adel Javanmard +5
The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite thi…
Private Training & Data Generation by Clustering Embeddings
Felix Zhou, Samson Zhou, Vahab Mirrokni +2
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this proc…
Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
Rachel Cummings, Alessandro Epasto, Jieming Mao +3
The turnstile continual release model of differential privacy captures scenarios where a privacy-preserving real-time analysis is sought for a dataset evolving through additions an…
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
Adel Javanmard, Rudrajit Das, Alessandro Epasto +1
Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance. While prior works ha…
Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures
Alina Ene, Alessandro Epasto, Vahab Mirrokni +4
In the maximum coverage problem we are given subsets from a universe , and the goal is to output subsets such that their union covers the largest possible number of di…