arXiv subjects

Yenan Wang

Publications and source records attributed to Yenan Wang.

4 recordsLinked to original sources

arXiv2026

From Bracha to Coded MBRB: Benchmarking Byzantine Reliable Broadcast Implementations

Byzantine Reliable Broadcast (BRB) and Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) are reliable-dissemination abstractions for fault-tolerant distributed systems. Yet their operational behavior is shaped not only by specifications and asymptotic communication bounds, but also by serialization, cryptography, buffering, orchestration, deployment environment, and fault-injection semantics. This paper implements and evaluates Bracha [Information and Computation, 1987], AFRT by Albouy et al. [TCS, 2023], and Coded MBRB by Albouy et al. [OPODIS, 2024]. We implement the algorithms in a shared Go codebase with common orchestration, instrumentation, parser-based specification checks, fault injection, and an open-source reproducibility artifact. The evaluation uses single-shot broadcasts in the Shadow network simulator, native profiling, a Google Cloud Platform deployment, and a distributed FABRIC testbed, covering controlled experiments up to 30 nodes, payloads up to 40 MB, 92,190 runs, and 2,361,600 parser-checked entries. The results show that Coded MBRB reduces transmitted data and improves latency in the evaluated cloud setting for larger payloads, but shifts cost to cryptographic and coding computation. Bracha and AFRT incur lower CPU costs at smaller payloads, but their full-payload dissemination increases processing, allocation, and network costs as payloads grow. Across the tested configurations, the parser found no duplicate deliveries, conflicting deliveries, or deliveries of values different from the sender's payload. The paper contributes implementation-level evidence and an extensible artifact for benchmarking BRB and MBRB as executable distributed-system components, exposing bottlenecks and operational trade-offs that are hidden by algorithmic descriptions alone.

cs.DC↗

arXiv2026

Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques

In federated learning (FL), balancing privacy protection, learning quality, and efficiency remains a challenge. Privacy protection mechanisms, such as Differential Privacy (DP), degrade learning quality, or, as in the case of Homomorphic Encryption (HE), incur substantial system overhead. To address this, we propose Alt-FL, a privacy-preserving FL framework that combines DP, HE, and synthetic data via a novel round-based interleaving strategy. Alt-FL introduces three new methods, Privacy Interleaving (PI), Synthetic Interleaving with DP (SI/DP), and Synthetic Interleaving with HE (SI/HE), that enable flexible quality-efficiency trade-offs while providing privacy protection. We systematically evaluate Alt-FL against representative reconstruction attacks, including Deep Leakage from Gradients, Inverting Gradients, When the Curious Abandon Honesty, and Robbing the Fed, using a LeNet-5 model on CIFAR-10 and Fashion-MNIST. To enable fair comparison between DP- and HE-based defenses, we introduce a new attacker-centric framework that compares empirical attack success rates across the three proposed interleaving methods. Our results show that, for the studied attacker model and dataset, PI achieves the most balanced trade-offs at high privacy protection levels, while DP-based methods are preferable at intermediate privacy requirements. We also discuss how such results can be the basis for selecting privacy-preserving FL methods under varying privacy and resource constraints.

cs.LG↗

arXiv2026

Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality

Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications. However, FL faces the dual challenge of ensuring learning quality and robust privacy protection while keeping resource consumption low, particularly when using computationally expensive techniques such as homomorphic encryption (HE). In this work, we enhance an FL process that preserves privacy using HE by integrating it with synthetic data generation and an interleaving strategy. Specifically, our solution, named Alternating Federated Learning (Alt-FL), consists of alternating between local training with authentic data (authentic rounds) and local training with synthetic data (synthetic rounds) and transferring the encrypted and plaintext model parameters on authentic and synthetic rounds (resp.). Our approach improves learning quality (e.g., model accuracy) through datasets enhanced with synthetic data, preserves client data privacy via HE, and keeps manageable encryption and decryption costs through our interleaving strategy. We evaluate our solution against data leakage attacks, such as the DLG attack, demonstrating robust privacy protection. Also, Alt-FL provides 13.4% higher model accuracy and decreases HE-related costs by up to 48% with respect to Selective HE.

cs.LG↗

arXiv2025

Towards a Formal Verification of Secure Vehicle Software Updates

With the rise of software-defined vehicles (SDVs), where software governs most vehicle functions alongside enhanced connectivity, the need for secure software updates has become increasingly critical. Software vulnerabilities can severely impact safety, the economy, and society. In response to this challenge, Strandberg et al. [escar Europe, 2021] introduced the Unified Software Update Framework (UniSUF), designed to provide a secure update framework that integrates seamlessly with existing vehicular infrastructures. Although UniSUF has previously been evaluated regarding cybersecurity, these assessments have not employed formal verification methods. To bridge this gap, we perform a formal security analysis of UniSUF. We model UniSUF's architecture and assumptions to reflect real-world automotive systems and develop a ProVerif-based framework that formally verifies UniSUF's compliance with essential security requirements - confidentiality, integrity, authenticity, freshness, order, and liveness - demonstrating their satisfiability through symbolic execution. Our results demonstrate that UniSUF adheres to the specified security guarantees, ensuring the correctness and reliability of its security framework.

cs.CR↗