arXiv subjects

Pengcheng Su

Publications and source records attributed to Pengcheng Su.

7 recordsLinked to original sources

arXiv2026

JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis

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 incident response, engineers need to know what evidence supported a diagnosis, which alternatives were considered, where contradictions remained, and whether the system resolved the case or preserved uncertainty. We address this gap with JustDiag, a diagnostic justification engine for RCA that maintains an explicit process state over evidence, findings, competing hypotheses, conflicts, and next checks. We evaluated the system on 66 real-world incidents using a two-layer protocol that separately scores final-answer quality and process quality. Relative to a matched control without diagnostic justification, JustDiag achieved stronger outcome and process scores, while accepting slightly lower terminal completion due to more calibrated non-closure. These results suggest that accountable RCA requires explicit diagnostic justification artifacts and process-aware evaluation, not only fluent final answers.

cs.SE↗

arXiv2026

VOW: Verifiable and Oblivious Watermark Detection for Large Language Models

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 model forces users to reveal potentially sensitive text to a provider for detection and offers no way to verify the integrity of the result. While asymmetric schemes have been proposed to address these issues, they are either impractical for short texts or lack formal guarantees linking watermark insertion and detection. We propose VOW, a new protocol that achieves both privacy-preserving and cryptographically verifiable watermark detection with high efficiency. Our approach formulates detection as a secure two-party computation problem, instantiating the watermark's core logic with a Verifiable Oblivious Pseudorandom Function (VOPRF). This allows the user and provider to perform detection without the user's text being revealed, while the provider's result is verifiable. Our comprehensive evaluation shows that VOW is practical for short texts and provides a crucial reassessment of watermark robustness against modern paraphrasing attacks.

cs.CR↗

arXiv2025

Mutual Information Bounds in the Shuffle Model

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 an information-theoretic perspective. We analyze two regimes: the shuffle-only setting, where each user directly submits its message ($Y_i=X_i$), and the shuffle-DP setting, where each user first applies a local $\varepsilon_0$-differentially private mechanism before shuffling ($Y_i=\mathcal{R}(X_i)$). Let $\boldsymbol{Z} = (Y_{\sigma(i)})_i$ denote the shuffled sequence produced by a uniformly random permutation $\sigma$, and let $K = \sigma^{-1}(1)$ represent the position of user 1's message after shuffling. For the shuffle-only setting, we focus on a tractable yet expressive \emph{basic configuration}, where the target user's message follows $Y_1 \sim P$ and the remaining users' messages are i.i.d.\ samples from $Q$, i.e., $Y_2,\dots,Y_n \sim Q$. We derive asymptotic expressions for the mutual information quantities $I(Y_1;\boldsymbol{Z})$ and $I(K;\boldsymbol{Z})$ as $n \to \infty$, and demonstrate how this analytical framework naturally extends to settings with heterogeneous user distributions. For the shuffle-DP setting, we establish information-theoretic upper bounds on total information leakage. When each user applies an $\varepsilon_0$-DP mechanism, the overall leakage satisfies $I(K; \boldsymbol{Z}) \le 2\varepsilon_0$ and $I(X_1; \boldsymbol{Z}\mid (X_i)_{i=2}^n) \le (e^{\varepsilon_0}-1)/(2n) + O(n^{-3/2})$. These results bridge shuffle differential privacy and mutual-information-based privacy.

cs.IT↗

arXiv2025

Bayesian Advantage of Re-Identification Attack in the Shuffle Model

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 first systematic study of the Bayesian advantage in re-identifying a user's message under the shuffle model. We begin with a basic setting: one sample is drawn from a distribution $P$, and $n - 1$ samples are drawn from a distribution $Q$, after which all $n$ samples are randomly shuffled. We define $\beta_n(P, Q)$ as the success probability of a Bayes-optimal adversary in identifying the sample from $P$, and define the additive and multiplicative Bayesian advantages as $\mathsf{Adv}_n^{+}(P, Q) = \beta_n(P,Q) - \frac{1}{n}$ and $\mathsf{Adv}_n^{\times}(P, Q) = n \cdot \beta_n(P,Q)$, respectively. We derive exact analytical expressions and asymptotic characterizations of $\beta_n(P, Q)$, along with evaluations in several representative scenarios. Furthermore, we establish (nearly) tight mutual bounds between the additive Bayesian advantage and the total variation distance. Finally, we extend our analysis beyond the basic setting and present, for the first time, an upper bound on the success probability of Bayesian attacks in shuffle differential privacy. Specifically, when the outputs of $n$ users -- each processed through an $\varepsilon$-differentially private local randomizer -- are shuffled, the probability that an attacker successfully re-identifies any target user's message is at most $e^{\varepsilon}/n$.

cs.CR↗

arXiv2025

Decomposition-Based Optimal Bounds for Privacy Amplification via Shuffling

Shuffling has been shown to amplify differential privacy guarantees, enabling a more favorable privacy-utility trade-off. To characterize and compute this amplification, two fundamental analytical frameworks have been proposed: the \emph{privacy blanket} by Balle et al. (CRYPTO 2019) and the \emph{clone}--including both the standard and stronger variant--by Feldman et al. (FOCS 2021, SODA 2023). These frameworks share a common foundation: decomposing local randomizers into structured components for analysis. In this work, we introduce a unified analytical framework--the general clone paradigm--which subsumes all possible decompositions, with the clone and blanket decompositions arising as special cases. Within this framework, we identify the optimal decomposition, which is precisely the one used by the privacy blanket. Moreover, we develop a simple and efficient algorithm based on the Fast Fourier Transform (FFT) to compute optimal privacy amplification bounds. Experimental results show that our computed upper bounds nearly match the lower bounds, demonstrating the tightness of our method. Building on this method, we also derive optimal amplification bounds for both \emph{joint} and \emph{parallel} compositions of LDP mechanisms in the shuffle model.

cs.CR↗

arXiv2023

An Information-theoretic Security Analysis of Honeyword

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 metrics--flatness and success-number--have been proposed and evaluated using various simulated attackers. Existing evaluations typically apply statistical learning methods to distinguish real passwords from decoys on real-world datasets. However, such evaluations may overestimate the system's security, as more effective distinguishing attacks could potentially exist. In this paper, we aim to analyze the security of Honeyword systems under the strongest theoretical attack, rather than relying on specific, expert-crafted attacks evaluated in prior experimental studies. We first derive mathematical expressions for the flatness and success-number under the strongest attack. We conduct analyses and computations for several typical scenarios, and determine the security of honeyword generation methods using a uniform distribution and the List model as examples. We further evaluate the security of existing honeyword generation methods based on password probability models (PPMs), which depends on the sample size used for training. We investigate, for the first time, the sample complexity of several representative PPMs, introducing two novel polynomial-time approximation schemes for computing the total variation between PCFG models and between higher-order Markov models. Our experimental results show that for small-scale password distributions, sample sizes on the order of millions--often tens of millions--are required to reduce the total variation below 0.1. A surprising result is that we establish an equivalence between flatness and total variation, thus bridging the theoretical study of Honeyword systems with classical information theory. Finally, we discuss the practical implications of our findings.

cs.CR↗

arXiv2020

Optical System Design of Bionic Compound Eye with Broad Field of View

In nature, many common insects have compound eyes composed of many small eyes arranged on a curved retina. This kind of vision systems have many advantages, such as small size, large FOV (field of view) and high sensitivity, which have attracted extensive attention and research from world-wide researchers. It has good application prospects in military strikes and mechanical vision. In this paper, a new type of miniature compound eye system with large FOV is designed, which contains a micro-lens array and a relay system. Hexagonal micro-lens array are spliced seamlessly as a curved shell in the designed compound eye system. The intermediate curved image formed by the curved array is converted to a planar image by introducing a relay system. After combination and optimization of the micro-lens array and the relay system, the MTF values at 89.3lp/mm for each FOV within 120.5° are greater than 0.3, and the corresponding RMS spot radii less than the radius of the Airy disk, which proves the good imaging quality for the compound eye. The clear aperture of a single micro lens is 250μm with FOV 6°. After tolerance analysis, the results show the image quality still holds good enough performance and meets the requirements of the additive manufacturing process.

physics.optics↗