arXiv subjects

Yuichi Sei

Publications and source records attributed to Yuichi Sei.

5 recordsLinked to original sources

arXiv2026

Statistical Analysis of Executability and Program Equivalence in Decompilation for IoT Vulnerability Detection

Internet of Things (IoT) devices handle sensitive privacy-related information such as user audio, video, and authentication data, making it essential to detect vulnerabilities in their firmware. Decompilation, a key detection technique, has recently attracted attention because Large Language Models (LLMs) enable high readability and high recompilation success rates. However, because LLM outputs depend on probabilistic token prediction, they tend to prioritize syntactic correctness and may generate plausible-looking code that is semantically different from the original binary. Vulnerabilities often arise in details that are easily lost in this process, such as error-handling flows and boundary checks. Existing evaluation metrics focus mainly on passing test cases and cannot sufficiently identify code whose internal structure has been altered despite appearing behaviorally valid, so a metric that quantifies the internal structure of decompiled code from multiple perspectives is needed. We propose a nine-dimensional quality evaluation metric consisting of three categories: structural, behavioral, and semantic similarity. Targeting 318 programs from OpenWrt, an open-source router platform underlying many commercial routers, we generated 19,625 decompilation results using five methods (one rule-based and four LLM-based) and analyzed them statistically. The recompilation-success group achieved significantly higher overall scores than the failure group (Cohen's d=0.92); behavioral similarity showed d=0.96 and structural similarity d=0.69, demonstrating that these metrics are important predictors of decompilation quality. This study provides a statistical evaluation foundation for quantifying implementation defects in IoT devices and a framework that generalizes to quality evaluation of black-box generative models.

cs.SE↗

arXiv2026

Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors

In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle protocols are vulnerable to collusion attacks by the data collector and users. They address this issue by introducing the augmented shuffle model that incorporates random sampling and dummy data addition into the shuffler. However, it remains open how to ensure the shuffler follows the protocol and does not collude with the data collector in this model. We address this trust issue by thoroughly exploring the augmented shuffle model with TEEs (Trusted Execution Environments). We first introduce a new privacy notion, FODP (Fully Oblivious DP), which strengthens DP to prevent various TEE side-channel attacks based on external/internal memory access patterns and control flows. We propose a general framework for FODP algorithms based on memory-size obfuscation and three concrete algorithms within it. We also improve the efficiency of our algorithms by using the count-min sketch and optimizing the number of hashes. We evaluate our algorithms on Intel SGX and demonstrate their effectiveness through comparisons with nine baselines.

cs.CR↗

arXiv2026

Gravity-Aware Hierarchical Routing for Lightweight SensorLLM on Human Activity Recognition

Recent studies on sensor-language alignment have shown that two-stage frameworks can improve the semantic modeling ability of wearable-sensor human activity recognition (HAR), where SensorLLM-style methods first perform motion-to-language alignment and then fine-tune the model for downstream tasks. However, our experiments reveal a consistent failure mode when the Stage 2 backbone is compressed to a compact model such as TinyLlama: recognition of dynamic activities remains relatively strong, while the discrimination of low-motion static classes such as standing, sitting, and lying degrades substantially. To address this issue, we propose a gravity-aware hierarchical routing head as a lightweight post-alignment adaptation built on top of an already aligned model, rather than a new large-scale pretraining framework. The method uses the per-channel mean and std from the Chronos tokenizer state to extract statistical cues related to posture and gravity direction, and adaptively combines a static expert and a full expert through soft routing, together with a load-balancing loss for stable training. On the MHealth dataset, this design significantly improves macro-F1 with minimal parameter overhead, and the gains are concentrated mainly on static classes while preserving strong performance on dynamic activities. As a first arXiv disclosure, the current paper reports results on a single dataset only, with the goal of highlighting the core method and laying the groundwork for broader evaluation in future work.

eess.SP↗

arXiv2025

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data

Shuffle DP (Differential Privacy) protocols provide high accuracy and privacy by introducing a shuffler who randomly shuffles data in a distributed system. However, most shuffle DP protocols are vulnerable to two attacks: collusion attacks by the data collector and users and data poisoning attacks. A recent study addresses this issue by introducing an augmented shuffle DP protocol, where users do not add noise and the shuffler performs random sampling and dummy data addition. However, it focuses on frequency estimation over categorical data with a small domain and cannot be applied to a large domain due to prohibitively high communication and computational costs. In this paper, we fill this gap by introducing a novel augmented shuffle DP protocol called the FME (Filtering-with-Multiple-Encryption) protocol. Our FME protocol uses a hash function to filter out unpopular items and then accurately calculates frequencies for popular items. To perform this within one round of interaction between users and the shuffler, our protocol carefully communicates within a system using multiple encryption. We also apply our FME protocol to more advanced KV (Key-Value) statistics estimation with an additional technique to reduce bias. For both categorical and KV data, we prove that our protocol provides computational DP, high robustness to the above two attacks, accuracy, and efficiency. We show the effectiveness of our proposals through comparisons with twelve existing protocols.

cs.CR↗

arXiv2025

Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy

The shuffle model of DP (Differential Privacy) provides high utility by introducing a shuffler that randomly shuffles noisy data sent from users. However, recent studies show that existing shuffle protocols suffer from the following two major drawbacks. First, they are vulnerable to local data poisoning attacks, which manipulate the statistics about input data by sending crafted data, especially when the privacy budget epsilon is small. Second, the actual value of epsilon is increased by collusion attacks by the data collector and users. In this paper, we address these two issues by thoroughly exploring the potential of the augmented shuffle model, which allows the shuffler to perform additional operations, such as random sampling and dummy data addition. Specifically, we propose a generalized framework for local-noise-free protocols in which users send (encrypted) input data to the shuffler without adding noise. We show that this generalized protocol provides DP and is robust to the above two attacks if a simpler mechanism that performs the same process on binary input data provides DP. Based on this framework, we propose three concrete protocols providing DP and robustness against the two attacks. Our first protocol generates the number of dummy values for each item from a binomial distribution and provides higher utility than several state-of-the-art existing shuffle protocols. Our second protocol significantly improves the utility of our first protocol by introducing a novel dummy-count distribution: asymmetric two-sided geometric distribution. Our third protocol is a special case of our second protocol and provides pure epsilon-DP. We show the effectiveness of our protocols through theoretical analysis and comprehensive experiments.

cs.CR↗