Grant, Verify, Revoke: A User-Centric Pattern for Blockchain Compliance

• URL: http://arxiv.org/abs/2603.15721v1
• Date: Mon, 16 Mar 2026 17:06:44 GMT
• Title: Grant, Verify, Revoke: A User-Centric Pattern for Blockchain Compliance
• Abstract summary: Decentralized web applications face an inherent conflict between public verifiability and personal privacy.<n>We introduce a Selective Disclosure Framework designed to restore user sovereignty by decoupling eligibility verification from identity revelation.<n>Our evaluation shows that client-side proof generation takes under 200ms, enabling a seamless interactive experience on commodity hardware.
• Score: 9.133320151595084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In decentralized web applications, users face an inherent conflict between public verifiability and personal privacy. To participate in regulated on-chain services, users must currently disclose sensitive identity documents to centralized intermediaries, permanently linking real-world identities to public transaction histories. This binary choice between total privacy loss or total exclusion strips users of agency and exposes them to persistent surveillance. In this work, we introduce a Selective Disclosure Framework designed to restore user sovereignty by decoupling eligibility verification from identity revelation. We present ZK-Compliance, a prototype that leverages browser-based zero-knowledge proofs to shift the interaction model, enabling users to prove specific attributes (e.g., "I am over 18") locally without revealing the underlying data. We implement a user-governed Grant, Verify, Revoke lifecycle that transforms the user's mental model of compliance from a permanent data handover into a dynamic, revocable authorization session. Our evaluation shows that client-side proof generation takes under 200ms, enabling a seamless interactive experience on commodity hardware. This work provides early evidence that regulatory compliance need not come at the cost of user privacy or autonomy.

Related papers

• Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle [65.6642776933861]
  This survey examines the protective paradigm that has grown around this intervention point.<n>We find that most protections are still validated mainly against static or weakly adaptive adversaries.<n>We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.
  arXiv  Detail & Related papers  (2026-08-05T00:46:50Z)
• Privacy-Preserving Compliance on Public Ledgers via Selective Disclosure Authorization Schemes [9.133320151595084]
  We develop a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness.<n>We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain address.<n>Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.
  arXiv  Detail & Related papers  (2026-06-18T09:38:13Z)
• KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation [72.01173512175531]
  KnowU-Bench is an online benchmark for personalized mobile agents.<n>It covers 42 general GUI tasks, 86 personalized tasks, and 64 proactive tasks.<n>Our experiments reveal a striking degradation: agents that excel at explicit task execution fall below 50% under vague instructions.
  arXiv  Detail & Related papers  (2026-04-09T16:50:50Z)
• Poster: Privacy-Preserving Compliance Checks on Ethereum via Selective Disclosure [9.47737368469032]
  This work proposes a general Selective Disclosure Framework built on, designed to decouple attribute verification from identity revelation.<n>By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents.<n>Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency.
  arXiv  Detail & Related papers  (2026-02-20T14:54:22Z)
• VeilAudit: Breaking the Deadlock Between Privacy and Accountability Across Blockchains [2.676349883103404]
  Cross chain interoperability in blockchain systems exposes a fundamental tension between user privacy and regulatory accountability.<n>We present VeilAudit, a cross chain auditing framework that introduces Auditor Only Linkability.<n>VeilAudit achieves this with a user generated Linkable Audit Tag that embeds a zero knowledge proof to attest to its validity without exposing the user master wallet address.
  arXiv  Detail & Related papers  (2025-10-14T05:16:23Z)
• Towards Privacy-Preserving Revocation of Verifiable Credentials with Time-Flexibility [0.36832029288386137]
  Self-Sovereign Identity (SSI) is an emerging paradigm for authentication and credential presentation.<n>The EUDI Digital Identity wallet is about to become a concrete implementation of this paradigm.<n>We propose the basis of a novel method that customizes the use of anonymous hierarchical identity-based encryption.
  arXiv  Detail & Related papers  (2025-03-27T21:58:32Z)
• Personalized Language Model Learning on Text Data Without User Identifiers [79.36212347601223]
  We propose to let each mobile device maintain a user-specific distribution to dynamically generate user embeddings.<n>To prevent the cloud from tracking users via uploaded embeddings, the local distributions of different users should either be derived from a linearly dependent space.<n> Evaluation on both public and industrial datasets reveals a remarkable improvement in accuracy from incorporating anonymous user embeddings.
  arXiv  Detail & Related papers  (2025-01-10T15:46:19Z)
• Towards an identity management solution on Arweave [0.0]
  This paper explores the potential of using Arweave network to develop an identity management solution.<n>By harnessing Arweave's permanent storage, our solution offers users Self-Sovereign Identity (SSI) framework.
  arXiv  Detail & Related papers  (2024-12-18T14:01:31Z)
• FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature [60.99054146321459]
  Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. We propose a cryptographic signature-based federated learning model ownership verification scheme named FedSOV.
  arXiv  Detail & Related papers  (2023-05-10T12:10:02Z)
• FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation [69.75513501757628]
  FedGT is a novel framework for identifying malicious clients in federated learning with secure aggregation. We show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al.
  arXiv  Detail & Related papers  (2023-05-09T14:54:59Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem [55.21502268698577]
  This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
  arXiv  Detail & Related papers  (2020-03-30T09:35:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.