5 papers
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
Joel Daniel Andersson, Palak Jain, Satchit Sivakumar
We study differentially-private statistics in the fully dynamic continual observation model, where many updates can arrive at each time step and updates to a stream can involve bot…
Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation
Palak Jain, Adam Smith, Connor Wagaman
We describe the first algorithms that satisfy the standard notion of node-differential privacy in the continual release setting (i.e., without an assumed promise on input streams).…
Synopsis: Secure and private trend inference from encrypted semantic embeddings
Madelyne Xiao, Palak Jain, Micha Gorelick +1
WhatsApp and many other commonly used communication platforms guarantee end-to-end encryption (E2EE), which requires that service providers lack the cryptographic keys to read comm…
An Empirical Study of Causal Relation Extraction Transfer: Design and Data
Sydney Anuyah, Jack Vanschaik, Palak Jain +2
We conduct an empirical analysis of neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embe…
Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning about Private Data Release
Mark Bun, Marco Carmosino, Palak Jain +2
The technical literature about data privacy largely consists of two complementary approaches: formal definitions of conditions sufficient for privacy preservation and attacks that…