← Search

Wennan Zhu

5 accepted papers

2023

Algorithms for bounding contribution for histogram estimation under user-level privacy

ICML 2023poster

We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of *all* entries of any single user. We consider the heterogeneous scenario where the quantity of data can be different for each user. In this scenario, the amount of noise i…

Cited by 10SourcePDFScholar
2023

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance

NeurIPS 2023poster

In federated frequency estimation (FFE), multiple clients work together to estimate the frequency of their local data by communicating with a server, while maintaining the security constraint of $\mathtt{secsum}$ where the server can only access the sum of client-held vectors. For FFE with a single…

Cited by 0SourcePDFScholar
2021

Forming Better Stable Solutions in Group Formation Games Inspired by Internet Exchange Points (IXPs)

AAAI 2021technical

We study a coordination game motivated by the formation of Internet Exchange Points (IXPs), in which agents choose which facilities to join. Joining the same facility as other agents you communicate with has benefits, but different facilities have different costs for each agent. Thus, the players wi…

Cited by 0SourcePDFScholar
2020

Federated Heavy Hitters Discovery with Differential Privacy

AISTATS 2020poster

The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care. To address these risks, we propose a distributed and privacy-preserving algorithm for discovering the…