Author name cluster

Qi Pang

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4 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Communication Bounds for the Distributed Experts Problem

Zhihao Jia
Qi Pang
Trung Tran
David Woodruff
Zhihao Zhang
Wenting Zheng

In this work, we study the experts problem in the distributed setting where an expert's cost needs to be aggregated across multiple servers. Our study considers various communication models such as the message-passing model and the broadcast model, along with multiple aggregation functions, such as summing and taking the $\ell_p$ norm of an expert's cost across servers. We propose the first communication-efficient protocols that achieve near-optimal regret in these settings, even against a strong adversary who can choose the inputs adaptively. Additionally, we give a conditional lower bound showing that the communication of our protocols is nearly optimal. Finally, we implement our protocols and demonstrate empirical savings on the HPO-B benchmarks.

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NeurIPS Conference 2024 Conference Paper

No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

Qi Pang
Shengyuan Hu
Wenting Zheng
Virginia Smith

Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. Watermarking, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating the misuse of such AI-generated content. However, we show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack---leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems, and propose guidelines and defenses for LLM watermarking in practice.

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ICML Conference 2023 Conference Paper

Secure Federated Correlation Test and Entropy Estimation

Qi Pang
Lun Wang 0001
Shuai Wang 0011
Wenting Zheng
Dawn Song

We propose the first federated correlation test framework compatible with secure aggregation, namely FED-$\chi^2$. In our protocol, the statistical computations are recast as frequency moment estimation problems, where the clients collaboratively generate a shared projection matrix and then use stable projection to encode the local information in a compact vector. As such encodings can be linearly aggregated, secure aggregation can be applied to conceal the individual updates. We formally establish the security guarantee of FED-$\chi^2$ by proving that only the minimum necessary information (i. e. , the correlation statistics) is revealed to the server. We show that our protocol can be naturally extended to estimate other statistics that can be recast as frequency moment estimations. By accommodating Shannon’e Entropy in FED-$\chi^2$, we further propose the first secure federated entropy estimation protocol, FED-$H$. The evaluation results demonstrate that FED-$\chi^2$ and FED-$H$ achieve good performance with small client-side computation overhead in several real-world case studies.

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NeurIPS Conference 2020 Conference Paper

Towards practical differentially private causal graph discovery

Lun Wang
Qi Pang
Dawn Song

Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data. Like other statistical data, a causal graph might leak sensitive information about participants in the dataset. In this paper, we present a differentially private causal graph discovery algorithm, Priv-PC, which improves both utility and running time compared to the state-of-the-art. The design of Priv-PC follows a novel paradigm called sieve-and-examine which uses a small amount of privacy budget to filter out “insignificant” queries, and leverages the remaining budget to obtain highly accurate answers for the “significant” queries. We also conducted the first sensitivity analysis for conditional independence tests including conditional Kendall’s τ and conditional Spearman’s ρ. We evaluated Priv-PC on 7 public datasets and compared with the state-of-the-art. The results show that Priv-PC achieves 10. 61 to 293. 87 times speedup and better utility. The implementation of Priv-PC, including the code used in our evaluation, is available at https: //github. com/sunblaze-ucb/ Priv-PC-Differentially-Private-Causal-Graph-Discovery.

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