13 citations · 50 across the 15 of their papers we have counts for
9 papers · 1 filter
Local Pan-Privacy for Federated Analytics
Vitaly Feldman, Audra McMillan, Guy N. Rothblum +1
Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system…
Private Selection with Heterogeneous Sensitivities
Daniela Antonova, Allegra Laro, Audra McMillan +1
Differentially private (DP) selection involves choosing a high-scoring candidate from a finite candidate pool, where each score depends on a sensitive dataset. This problem arises…
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan +1
A key challenge in many modern data analysis tasks is that user data are heterogeneous. Different users may possess vastly different numbers of data points. More importantly, it ca…
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
Private Federated Statistics in an Interactive Setting
Audra McMillan, Omid Javidbakht, Kunal Talwar +21
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interacti…
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
Vitaly Feldman, Audra McMillan, Kunal Talwar
The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key re…
Non-parametric Differentially Private Confidence Intervals for the Median
Joerg Drechsler, Ira Globus-Harris, Audra McMillan +2
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper…
Property Testing for Differential Privacy
Anna Gilbert, Audra McMillan
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular,…
Local Differential Privacy for Physical Sensor Data and Sparse Recovery
Anna C. Gilbert, Audra McMillan
In this work we explore the utility of locally differentially private thermal sensor data. We design a locally differentially private recovery algorithm for the 1-dimensional, disc…