We study fine-grained error bounds for differentially private algorithms for counting under
continual observation. Our main insight is that the matrix mechanism when using lower …

The first large-scale deployment of private federated learning uses differentially private
counting in the continual release model as a subroutine (Google AI blog titled “Federated …

Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph
representations such as node ranking, labeling, and graph embedding. However, while data …

We study the problem of maintaining a differentially private decaying sum under continual
observation. We give a unifying framework and an efficient algorithm for this problem for any …

In the foundational work of Dwork et al.[15] on continual observation under differential
privacy (DP), two privacy models have been proposed: event-level DP and user-level DP …

We study fine-grained error bounds for differentially private algorithms for averaging and
counting in the continual observation model. For this, we use the completely bounded …

In graph machine learning, data collection, sharing, and analysis often involve multiple
parties, each of which may require varying levels of data security and privacy. To this end …

We perform a rigorous study of private matrix analysis when only the last $ W $ updates to
matrices are considered useful for analysis. We show the existing framework in the non …

We propose an efficient ɛ-differentially private algorithm, that given a simple weighted n-
vertex, m-edge graph G with a maximum unweighted degree Δ (G)≤ n-1, outputs a synthetic …

We describe the first algorithms that satisfy the standard notion of node-differential privacy in
the continual release setting (ie, without an assumed promise on input streams). Previous …