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 …
JD Andersson, R Pagh - Advances in Neural Information …, 2024 - proceedings.neurips.cc
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 …
W Dong, Q Luo, K Yi - 2023 IEEE Symposium on Security and …, 2023 - ieeexplore.ieee.org
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 …
J Upadhyay, S Upadhyay - International Conference on …, 2021 - proceedings.mlr.press
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 …