Tianhao Wang

Estimating Numerical Distributions under Local Differential Privacy

2019 · arXiv (Cornell University)

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator. We study the problem of recovering the distribution …

Federated Boosted Decision Trees with Differential Privacy

2022 · Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security

There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different …

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

2023 · arXiv (Cornell University)

We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We propose a provably efficient algorithm based on value iteration that enable …

Neural Scoring: A Refreshed End-to-End Approach for Speaker Recognition in Complex Conditions

2024 · arXiv (Cornell University)

Modern speaker verification systems primarily rely on speaker embeddings, followed by verification based on cosine similarity between the embedding vectors of the enrollment and test utterances. While effective, these methods struggle with multi-talker speech due …

SPAS: Continuous Release of Data Streams under w-Event Differential Privacy

2025 · Proceedings of the ACM on Management of Data

Continuous release of data streams is frequently used in numerous applications. However, when data is sensitive, this poses privacy risks. To mitigate this risk, efforts have been devoted to devising techniques that satisfy a formal …