6 papers
JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis
Tingzhu Bi, Xinrui Jiang, Xun Zhang +5
Large language models can produce fluent root cause analyses, but fluent final answers alone are insufficient evidence for accountability in high-stakes operations. In real inciden…
VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
Xiaokun Luan, Yihao Zhang, Pengcheng Su +2
Large Language Model (LLM) watermarking is crucial for establishing the provenance of machine-generated text, but most existing methods rely on a centralized trust model. This mode…
Mutual Information Bounds in the Shuffle Model
Pengcheng Su, Haibo Cheng, Ping Wang
The shuffle model enhances privacy by anonymizing users' reports through random permutation. This paper presents the first systematic study of the single-message shuffle model from…
Decomposition-Based Optimal Bounds for Privacy Amplification via Shuffling
Pengcheng Su, Haibo Cheng, Ping Wang
Shuffling has been shown to amplify differential privacy guarantees, enabling a more favorable privacy-utility trade-off. To characterize and compute this amplification, two fundam…
Bayesian Advantage of Re-Identification Attack in the Shuffle Model
Pengcheng Su, Haibo Cheng, Ping Wang
The shuffle model, which anonymizes data by randomly permuting user messages, has been widely adopted in both cryptography and differential privacy. In this work, we present the fi…
An Information-theoretic Security Analysis of Honeyword
Pengcheng Su, Haibo Cheng, Wenting Li +1
Honeyword is a representative "honey" technique that employs decoy objects to mislead adversaries and protect the real ones. To assess the security of a Honeyword system, two metri…