4 citations · 10 across the 16 of their papers we have counts for
5 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…
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
Hilal Asi, Vitaly Feldman, Hannah Keller +2
We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradient…
Wally: Batched Private Nearest Neighbor Search at Scale
Hilal Asi, Fabian Boemer, Nicholas Genise +8
We present Wally, a batched private nearest-neighbor search protocol that uses differential privacy to break the linear computation barrier of fully-oblivious schemes. In Tiptoe, t…
PINE: Efficient Norm-Bound Verification for Secret-Shared Vectors
Guy N. Rothblum, Eran Omri, Junye Chen +1
Secure aggregation of high-dimensional vectors is a fundamental primitive in federated statistics and learning. A two-server system such as PRIO allows for scalable aggregation of…
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…