부산대학교 도서관

Metaverse provides human-centric immersive communication experiences where humans can teleport across different virtual landscapes and build real-time communications via digital identities with others in the same landscape. Despite great benefits, a natural question in human-centric metaverse communications is how to secure digital content among humans. In this regard, blockchain has been widely applied due to its distinct features (e.g., decentralization, transparency, and immutability). Unfortunately, the inherent properties of the blockchain also hinder humans from further deploying preferences to flexibly govern the digital content (i.e., who can read and who can edit), limiting human-centric communication abilities. Some redactable blockchain-based solutions have been proposed, but most of them suffer from the issues of data and preference leakage. To address the issues, we propose a privacy-preserving identity-based data governance (IDRG) scheme for blockchain-empowered human-centric metaverse communications. Combining digital identities, IDRG cryptographically allows humans to govern readability and editability with the right downward compatibility (i.e., humans with editability are endowed with readability) while protecting policy privacy. Specifically, IDRG leverages the polynomial function technique to break through the bottleneck of the traditional identity-based encryption technique (i.e., a policy only contains a user) to achieve a policy for multiple users. Subsequently, the optimized policies are utilized to enrich chameleon hash-based redactable blockchains for comprehensive rights governance. Further, IDRG supports user accountability and revocation by combining the proxy re-encryption technique. Security analysis proves the security of IDRG under the chosen-ciphertext attack. Experiments on the FISCO blockchain platform demonstrate that IDRG requires approximately 0.1 s to process an encryption request, 0.01 s for a reading request, and 1 s for an editing request. Overall, IDRG achieves a $3\times $ reduction in computational costs compared with state-of-the-art solutions.