Web 3.0 is a grand design with intricate data interchange, implying the requirement of versatile network protocol to ensure its security. Attribute-based signature (ABS) allows a user, who is featured with a set of attributes, to sign messages under a predicate. The validity of the ABS signature demonstrates that this signature is generated by the user whose attributes satisfy the corresponding predicate, and thus flexibly achieves anonymous authentication. Similar to other digital signatures, the security of ABS is broken in case the private key of the user is leaked out. To address the threat brought by the key leakage, this paper proposes a puncturable attribute-based signature scheme that allows the private key generator to revoke the signing right associated with specific tags. This paper firstly elaborates the construction of the proposed ABS scheme with puncturable property, and then proves its security theoretically by reducing the involved security to the computational Diffie–Hellman assumption. This paper then experimentally shows that the suggested puncturable ABS scheme owns a more efficient storage cost and superior performance.

The increasing popularity of virtual reality (VR) has stressed the importance of authenticating VR users while preserving their privacy. Behavioral biometrics, owing to their robustness and ease of collection, compared to traditional modes such as passwords, have become a favored authentication choice. While current approaches that utilize behavioral biometrics to train classifiers for authentication yield promising accuracy, they cause privacy breaches by sharing sensitive data with a server to train a central model. In this paper, we present MetaFL, a first-of-its-kind privacy-preserving VR authentication framework that leverages federated learning (FL) on multi-modal motion data. The design of MetaFL is motivated by our key insight that various modalities of motion data uniquely affect authentication performance for individual users and among different users. It is attributed to the fundamental challenge of privacy-preserving user authentication: users can access only their own data with limited global knowledge. To tackle this issue, MetaFL judiciously selects the most suitable modalities for each user, which is decomposed into within-user ordering and between-user selection to eliminate the complex interplay between various conflicting factors. Moreover, we develop a personalized strategy to initialize FL models, further improving authentication accuracy. Our extensive performance evaluation on six public datasets shows that MetaFL outperforms state-of-the-art FL-based models (e.g., 17--28% higher authentication accuracy), and its accuracy gap with the non-privacy-preserving central model is small (i.e., only <2%).