Xuekai Liu

Ryan Lavin, Xuekai Liu, Hardhik Mohanty et al.

Zero-knowledge proofs (ZKPs) enable computational integrity and privacy by allowing one party to prove the truth of a statement without revealing underlying data. Compared with alternatives such as homomorphic encryption and secure multiparty computation, ZKPs offer distinct advantages in universality and minimal trust assumptions, with applications spanning blockchain systems and confidential verification of computational tasks. This survey provides a technical overview of ZKPs with a focus on an increasingly relevant subset called zkSNARKs. Unlike prior surveys emphasizing algorithmic and theoretical aspects, we take a broader view of practical deployments and recent use cases across multiple domains including blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications such as voting, authentication, timelocks, and machine learning. To support consistent comparison, we provide (i) a taxonomy of application areas, (ii) evaluation criteria including proof size, prover and verifier time, memory, and setup assumptions, and (iii) comparative tables summarizing key tradeoffs and representative systems. The survey also covers supporting infrastructure, including zero-knowledge virtual machines, domain-specific languages, libraries, and frameworks. While emphasizing zkSNARKs for their prevalence in deployed systems, we compare them with zkSTARKs and Bulletproofs to clarify transparency and performance tradeoffs. We conclude with future research and application directions.

Shiyu Fang, Xiaocong Zhao, Xuekai Liu et al.

Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficiency through cooperative decision-making. However, existing methods often overlook the individual needs and heterogeneity of cooperative participants, making it difficult to transfer them to environments where they coexist with human-driven vehicles (HDVs).To address this challenge, this paper proposes an adaptive potential game (APG) cooperative driving framework. First, the system utility function is established on the basis of a general form of individual utility and its monotonic relationship, allowing for the simultaneous optimization of both individual and system objectives. Second, the Shapley value is introduced to compute each vehicle's marginal utility within the system, allowing its varying impact to be quantified. Finally, the HDV preference estimation is dynamically refined by continuously comparing the observed HDV behavior with the APG's estimated actions, leading to improvements in overall system safety and efficiency. Ablation studies demonstrate that adaptively updating Shapley values and HDV preference estimation significantly improve cooperation success rates in mixed traffic. Comparative experiments further highlight the APG's advantages in terms of safety and efficiency over other cooperative methods. Moreover, the applicability of the approach to real-world scenarios was validated through field tests.

Zhikai Li, Xuekai Liu, Boyuan Xu et al.

Blockchain systems face significant scalability challenges due to growing data volumes and increasing transaction demands, necessitating more efficient data structures and verification mechanisms. Verkle trees, a novel data structure combining the efficiency of Merkle trees with the compactness of vector commitments, have gained attention for their potential to optimize blockchain storage and improve scalability. However, their practical implementation, especially at the smart contract level, has remained unexplored. To address these challenges, we present TS-verkle, the first known TypeScript-native implementation of Verkle trees designed for web3 backend compatibility, coupled with a corresponding on-chain verifier written in Solidity. Our work bridges this gap by providing a concrete implementation of Verkle trees and demonstrating their feasibility for on-chain verification. While previous literature suggests Verkle trees should outperform Merkle trees due to their succinct proof size, our empirical evaluation reveals that basic implementations of Verkle trees actually incur higher costs than Merkle trees without advanced optimization techniques. This finding represents a crucial insight for blockchain developers and researchers considering Verkle tree adoption. The paper discusses implementation strategies and performance characteristics while exploring implications for scaling and data availability in decentralized blockchain systems.