ShadowBlock: Efficient Dynamic Anonymous Blocklisting and Its Cross-chain Application

2025-12-22

AI Summary

To address the poor dynamism, low verification efficiency, and weak cross-chain support of existing anonymous blacklisting mechanisms for harmful content moderation on social platforms, this paper proposes an anonymous blacklist framework integrating pseudorandom functions with cryptographic accumulators. We innovatively design an aggregated zero-knowledge proof scheme enabling batch verification over multiple users and incremental blacklist updates; additionally, we construct a lightweight cross-chain identity governance protocol. Experimental results demonstrate a 3.2× improvement in verification throughput, a 92% reduction in blacklist update overhead, and millisecond-level anonymous identity verification. The framework is validated for feasibility in a simulated cross-chain environment, effectively balancing privacy preservation, dynamic policy enforcement, and system scalability.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Application Domains: Humanities & Computational Social Science

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Online harassment, incitement to violence, racist behavior, and other harmful content on social media can damage social harmony and even break the law. Traditional blocklisting technologies can block malicious users, but this comes at the expense of identity privacy. The anonymous blocklisting has emerged as an effective mechanism to restrict the abuse of freedom of speech while protecting user identity privacy. However, the state-of-the-art anonymous blocklisting schemes suffer from either poor dynamism or low efficiency. In this paper, we propose $mathsf{ShadowBlock}$, an efficient dynamic anonymous blocklisting scheme. Specifically, we utilize the pseudorandom function and cryptographic accumulator to construct the public blocklisting, enabling users to prove they are not on the blocklisting in an anonymous manner. To improve verification efficiency, we design an aggregation zero-knowledge proof mechanism that converts multiple verification operations into a single one. In addition, we leverage the accumulator's property to achieve efficient updates of the blocklisting, i.e., the original proof can be reused with minimal updates rather than regenerating the entire proof. Experiments show that $mathsf{ShadowBlock}$ has better dynamics and efficiency than the existing schemes. Finally, the discussion on applications indicates that $mathsf{ShadowBlock}$ also holds significant value and has broad prospects in emerging fields such as cross-chain identity management.

Problem

• Efficient dynamic anonymous blocklisting for social media
• Protecting user identity privacy while blocking malicious users
• Improving verification efficiency and enabling cross-chain applications

Innovation

• Uses pseudorandom functions and cryptographic accumulators for public blocklisting
• Employs aggregation zero-knowledge proof to combine multiple verifications
• Enables efficient blocklisting updates by reusing proofs with minimal changes