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 …

The streaming model of computation is a popular approach for working with large-scale
data. In this setting, there is a stream of items and the goal is to compute the desired …

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 privacy under continual observation we study how to release differentially private
estimates based on a dataset that evolves over time. The problem of releasing private prefix …

We study the space complexity of the two related fields of differential privacy and adaptive
data analysis. Specifically, Under standard cryptographic assumptions, we show that there …

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 …

We study the fundamental problem of frequency estimation under both privacy and
communication constraints, where the data is distributed among $ k $ parties. We consider …

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 …