arXiv subjects

Quanwei Cai

Publications and source records attributed to Quanwei Cai.

5 recordsLinked to original sources

arXiv2026

Enforcing Cryptographic Distributed-VCS Access Control with No Trust on Servers

Version control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage changes to software code and various types of documents. Unlike CVCS, where entities obtain data from a central server, each entity in DVCS stores the entire repository and shares it independently. In VCS, existing access control schemes require the participation of a central server and cannot be deployed in a completely distributed scenario. Additionally, these schemes often fail to enforce fine-grained access control for write permissions, which is crucial for collaborative work in a distributed environment. In this paper, we propose a distributed version control system access control scheme (named DVAC), which enforces cryptographic access control on distributed user nodes based on attribute-based encryption (ABE) and attribute-based signature (ABS). DVAC is designed to enforce a cryptographic access control protocol for DVCS, which enables file granularity read and write separation access control without the support of a central server. To ensure the integrity of the core version control functions in DVCS while protecting data security, DVAC incorporates a version control adaptation protocol. Additionally, DVAC leverages Ethereum smart contracts to maintain access control policies, ensuring distributed storage and trusted management of access policies. The architecture of DVAC is designed to seamlessly integrate with existing mature DVCS, such as Git, with minimal modifications. We have implemented a prototype of DVAC and integrated it with Git. A comprehensive performance evaluation was conducted to assess the overhead introduced by DVAC, and it was demonstrated that the overhead is modest.

cs.CR↗

arXiv2026

Enforcing Access Control in Distributed Version Control Systems

Version control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage the changes to various types of data. Unlike the CVCS where all the entities obtain the data from the server and the access control is enforced with the cooperation of the server, each entity in the DVCS stores the entire repository, obtains the repository shared by any entity and is free to share its own repository. Therefore, existing access control schemes for CVCS are not suitable for DVCS. In this paper, we present a distributed access control scheme (Disac) for DVCS. Disac makes each entity have full control over its data, while the access control is enforced at each entity independently. We adopt Attribute-based Encryption (ABE) and Attribute-based Signature (ABS) to achieve the read and write permission control. The analysis of the Git client demonstrates that Disac can be integrated easily.

cs.CR↗

arXiv2025

Understanding the Identity-Transformation Approach in OIDC-Compatible Privacy-Preserving SSO Services

OpenID Connect (OIDC) enables a user with commercial-off-the-shelf browsers to log into multiple websites, called relying parties (RPs), by her username and credential set up in another trusted web system, called the identity provider (IdP). Identity transformations are proposed in UppreSSO to provide OIDC-compatible SSO services, preventing both IdP-based login tracing and RP-based identity linkage. While security and privacy of SSO services in UppreSSO have been proved, several essential issues of this identity-transformation approach are not well studied. In this paper, we comprehensively investigate the approach as below. Firstly, several suggestions for the efficient integration of identity transformations in OIDC-compatible SSO are explained. Then, we uncover the relationship between identity-transformations in SSO and oblivious pseudo-random functions (OPRFs), and present two variations of the properties required for SSO security as well as the privacy requirements, to analyze existing OPRF protocols. Finally, new identity transformations different from those designed in UppreSSO, are constructed based on OPRFs, satisfying different variations of SSO security requirements. To the best of our knowledge, this is the first time to uncover the relationship between identity transformations in OIDC-compatible privacy-preserving SSO services and OPRFs, and prove the SSO-related properties (i.e., key-identifier freeness, RP designation and user identification) of OPRF protocols, in addition to the basic properties of correctness, obliviousness and pseudo-randomness.

cs.CR↗

arXiv2021

UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services

Single sign-on (SSO) allows a user to maintain only the credential for an identity provider (IdP) to log into multiple relying parties (RPs). However, SSO introduces privacy threats, as (a) a curious IdP could track a user's all visits to RPs, and (b) colluding RPs could learn a user's online profile by linking her identities across these RPs. This paper presents a privacypreserving SSO scheme, called UPPRESSO, to protect an honest user's online profile against (a) an honest-but-curious IdP and (b) malicious RPs colluding with other users. UPPRESSO proposes an identity-transformation approach to generate untraceable ephemeral pseudo-identities for an RP and a user from which the target RP derives a permanent account for the user, while the transformations also provide unlinkability. This approach protects the identities of the user and the target RPs in a login flow, while working compatibly with widely-deployed SSO protocols and providing services accessed from a commercial-off-the-shelf browser without plug-ins or extensions. We built a prototype of UPPRESSO on top of MITREid Connect, an open-source SSO system. The extensive evaluations show that it fulfills the security and privacy requirements of SSO with reasonable overheads.

cs.CR↗

arXiv2021

FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated learning remains limited due to high cost incurred by conventional privacy-preserving methods. To address the problem, we propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy, and empirically evaluated on real-world and synthetic datasets.

cs.LG↗