The most efficient asynchronous Byzantine fault tolerant (BFT) framework innagreement settings is due to Ben-Or, Kemler, and Rabin (BKR), wherenis the total number of replicas. Despite recent efforts to bring BKR closer to practical deployment, state-of-the-art designs are still hampered by the inherent mutual waiting between the broadcast and agreement phases. In response, we propose ByzTopia, a new asynchronous BFT protocol that removes this performance bottleneck. Its technical core is to decouple these two phases without introducing additional cryptographic primitives. To enable a more efficient decoupling of the broadcast and agreement phases, we introduce multi-shot reliable broadcast (MRBC), which ensures that replicas deliver messages in a well-ordered sequence across consecutive slots. We implement ByzTopia and evaluate it in various settings. Experimental results show that ByzTopia achieves up to 6.54× the throughput of PACE (for n == 31), the state-of-the-art asynchronous BFT of the same type, and 2.65× that of FIN (for n = 16), the state-of-the-art signature-free asynchronous BFT.

Anonymous credential systems (ACs) enable users to selectively disclose attributes in an anonymous and unlinkable manner, but their misuse is hard to address without traceability. Existing traceable ACs introduce regulators to handle this issue, but they impose substantial communication and computational overhead on regulators, especially in complex scenarios involving large numbers of authentication records and multiple independent regulators. In this work, we propose Public-Key Encryption with Equality Test and Variable Public Generator (PKEET-VPG) and its verifiable version (VPV) for adapting to traceable ACs to support secure outsourceable record retrieval from regulators to service providers. The core technical idea is a session-user-level tracing key whose validity is bound to a single authentication session, thereby preventing the abuse of tracing keys.We formally prove that both schemes achieve OW-CCA2 security, and show that, when integrated into ACs, they preserve user anonymity, support non-frameability, and retain cross-session unlinkability. Theoretical analysis and comparative experiments demonstrate that our schemes can reduce the overhead of regulators at a moderate cost in the authentication phase. Furthermore, our schemes support session-level ciphertext deduplication, which may be of independent interest in some scenarios, such as anonymous voting or one-action-per-session authentication.

The widespread deployment of Internet of Things (IoT) devices has driven their segmentation into distinct trust domains for the purpose of governance, creating a critical need for secure cross-domain authentication (CDA). CDA must preserve both anonymity and traceability of device identities to enable trustworthy data exchange. However, existing approaches, while exploring this trade-off, remain vulnerable to single points of failure and Sybil attacks—threats that are especially severe for unattended and resource-constrained devices. In this paper, we propose a Self-Sovereign and Supervised Cross-domain authentication scheme (SCross) to tackle these issues. The main building block we designed is a pseudonym management scheme (PMS) that allows devices to generate and use pseudonyms without relying on a trusted party. Although devices has full control of their identities, PMS still ensures traceability, Sybil resistance, and revocability. We define the formal security models of PMS, instantiate it under two different approaches, namely group signature (SCross-GS) and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs, SCross-ZK), and present security proofs for our proposal. We implemented and evaluated SCross. The result shows that our scheme achieves an effective trade-off between security and efficiency.

Asynchronous common subset (ACS) is an essential building block for Byzantine fault-tolerance and multi-party computation. The classic ACS framework is due to Ben-Or, Kemler, and Rabin (BKR), consisting of${n}$reliable broadcast (RBC) instances and${n}$asynchronous binary agreement (ABA) instances (where${n}$is the total number of replicas). Despite recent progresses of practical BKR-ACS, the state-of-the-art designs are still trapped by a validity-latency tradeoff. In this paper, we propose Sweeper, a new ACS protocol that breaks the tradeoff, achieving optimal validity and latency. Moreover, Sweeper maintains other benefits including optimal resilience, signature-free, and information-theoretic settings. Sweeper is built on RBC and composable biased reproposable ABA (CBiased RABA). Different from the conventional RABA, CBiased RABA allows replicas to be more biased towards specific RABA instances. We provide generic strategies to transform existing ABA/RABA protocols and a new RABA protocol that we introduce, to CBiased RABA. Furthermore, Sweeper can achieve up to$2 \times $the throughput of PACE-ACS (CCS 2022), the state-of-the-art ACS protocol that follows the BKR-ACS framework.

Deep neural network model is the core technology for sensors of the autonomous driving platform to perceive the external environment. Recent research have shown that it has a certain vulnerability. The artificial designed adversarial examples can make the DNN model output the wrong results. These adversarial examples not only exist in the digital world, but also in the physical world. At present, research on autonomous driving platform mainly focus on attacking a single sensor. In this paper, we introduce Camdar-adv, a method for generating image adversarial examples on three-dimensional (3D) objects, which could potentially lunch a multisensor attack toward the autonomous driving platforms. Specifically, with objects that can attack LiDAR sensors, a geometric transformation can be used to project their shape onto the two-dimensional plane. Adversarial perturbations against optical image sensor could be added to the surface of the adversarial 3D objects precisely without changing its geometry. Test results on the open-source autonomous driving data set KITTI show that Camdar-adv can generate adversarial samples for the state of the art object detection model. From a fixed viewpoint, our method can achieve an attack success rate over 99%.