Metaverse allows users to immersively interact with millions of partners, breaking through the constraints of the physical world. To ensure the traceability of malicious avatars, provers in large-scale commercial activities need to periodically submit identity parameters to the verifier as interaction evidence, imposing a disastrous communication and storage burden on both parties. In this article, we propose a traceable and efficient continuous authentication scheme for large-scale avatars. First, we construct a continuous authentication framework, where the prover’s device locally checks its manipulator to submit the starting and ending identities as interaction evidence. Second, we propose a transitive signature scheme with path verifiability to ensure the relevance and unforgeability of the starting and ending identities. Finally, we design a traceable authentication protocol based on the proposed signature scheme to reduce communication and storage costs while guaranteeing traceability. Security analysis shows that the protocol not only defends against the attacks of device hijacking and false accusations but also supports virtual-to-physical tracking. Extensive evaluations show that the communication cost and storage costs are reduced by 95.97% and 98.35%, respectively, which can be used for avatar authentication and tracking in large-scale commercial activities.

Learned indexes provide significant performance advantages over classical ordered indexes. However, current learned indexes face challenges regarding tradeoffs between performance and space, as well as scalability issues in platforms with multiple NUMA nodes. These limitations hinder the practical application of learned indexes in production environments. This article presents DiffLex, a learned index with high-performance, memory-efficiency, and NUMA-awareness. The core design of DiffLex is to perform differentiated management based on the popularity of data. For optimal performance, DiffLex stores newly inserted data in sparse delta arrays and frequently accessed data in sparse hot cache arrays. However, for cold data that occupy a majority of the storage space, DiffLex stores them in dense arrays and conducts compression to reduce memory costs. DiffLex ensures NUMA-awareness by partitioning sparse deltas and replicating the hot cache arrays across multiple NUMA nodes. Additionally, we propose a persistent version of DiffLex tailored for emerging persistent memory devices. Our evaluation results demonstrate that DiffLex achieving 3.88× and 1.82× performance improvements compared to state-of-the-art learned indexes, while maintaining a compact index size.

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being detected.In this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven avatars.Security analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars.

Metaverse is a vast virtual world parallel to the physical world, where the user acts as an avatar to enjoy various services that break through the temporal and spatial limitations of the physical world. Metaverse allows users to create arbitrary digital appearances as their own avatars by which an adversary may disguise his/her avatar to fraud others. In this paper, we propose an anti-disguise authentication method that draws on the idea of the first impression from the physical world to recognize an old friend. Specifically, the first meeting scenario in the metaverse is stored and recalled to help the authentication between avatars. To prevent the adversary from replacing and forging the first impression, we construct a chameleon-based signcryption mechanism and design a ciphertext authentication protocol to ensure the public verifiability of encrypted identities. The security analysis shows that the proposed signcryption mechanism meets not only the security requirement but also the public verifiability. Besides, the ciphertext authentication protocol has the capability of defending against the replacing and forging attacks on the first impression. Extensive experiments show that the proposed avatar authentication system is able to achieve anti-disguise authentication at a low storage consumption on the blockchain.

Learned indexes that utilize machine learning models can offer significant performance advantages over traditional indexes. However, existing learned indexes suffer from space-performance tradeoffs and they cannot scale well in multiple NUMA-nodes machines. These issues limit the development of learned indexes in production environments. In this paper, we propose DiffLex, a high-performance, memory-efficient and NUMA-aware learned index. The core idea of DiffLex is to differentiate key management based on hotness. To achieve high performance, DiffLex stores newly inserted keys in sparse deltas and frequently accessed keys in a sparse hot cache. For cold keys that take up most of the storage space, however, DiffLex stores them in dense arrays to save memory costs. DiffLex also makes sparse deltas and hot cache NUMA-aware by partitioning sparse deltas and replicating hot cache across different NUMA nodes. Our evaluation shows that DiffLex outperforms the state-of-the-art ALEX by 3.88x and 1.82x for insert and search operations, respectively, while maintaining a small index size.

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on biometric-based authentication and chameleon signature. First, aiming at disguise in virtual space, we design an avatar’s two-factor identity model to ensure the verifiability of avatar’s virtual identity and physical identity. Second, facing at authentication efficiency and keys holding cost, we propose a chameleon collision signature algorithm to efficiently ensure that the avatar’s virtual identity is associated with its physical identity. Finally, aiming at impersonation in the physical world, we design two decentralized authentication protocols based on the avatar’s identity model and the chameleon collision signature to achieve real-time authentication on the avatar’s identity. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of the avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves virtual-physical tracking.