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Protecting the 'Stop Using My Data' Right through Blockchain-assisted Evidence Generation

Fan Zhang, Peng Liu

TL;DR

This paper tackles enforcing the 'stop using my data' right after behavioral data have been collected, a scenario where traditional preventive technologies fall short. It proposes a blockchain-assisted Evidence Generation Framework that leverages Web3 communities and secure MPC to generate verifiable evidence of post-request data usage, guided by a two-phase evidence generation protocol and a supporting Lemma. The authors demonstrate the approach in a recommendation-system case study, achieving near-perfect probing success rates and a measured effectiveness exceeding $99\%$. By providing an immutable evidentiary trail and privacy-preserving collaboration, the framework offers a practical mechanism to deter violations and support judicial verification, with potential applicability to diverse online platforms. The work advances data-right protection by integrating legal concepts with decentralized, cryptographic tooling for post-acquisition governance.

Abstract

In order to provide personalized services to users, Internet-based platforms collect and utilize user-generated behavioral data. Although the 'stop using my data' right should be a fundamental data right, which allows individuals to request their personal data to be no longer utilized by online platforms, the existing preventive data protection measures (e.g., cryptographic data elimination, differential privacy) are unfortunately not applicable. This work aims to develop the first Evidence Generation Framework for deterring post-acquisition data right violations. We formulated the 'stop using my data' problem, which captures a vantage facet of the multi-faceted notion of 'right to be forgotten'. We designed and implemented the first blockchain-assisted system to generate evidence for deterring the violations of the 'stop using my data' right. Our system employs a novel two-stage evidence generation protocol whose efficacy is ensured by a newly proposed Lemma. To validate our framework, we conducted a case study on recommendation systems with systematic evaluation experiments using two real-world datasets: the measured success rate exceeds 99%.

Protecting the 'Stop Using My Data' Right through Blockchain-assisted Evidence Generation

TL;DR

This paper tackles enforcing the 'stop using my data' right after behavioral data have been collected, a scenario where traditional preventive technologies fall short. It proposes a blockchain-assisted Evidence Generation Framework that leverages Web3 communities and secure MPC to generate verifiable evidence of post-request data usage, guided by a two-phase evidence generation protocol and a supporting Lemma. The authors demonstrate the approach in a recommendation-system case study, achieving near-perfect probing success rates and a measured effectiveness exceeding . By providing an immutable evidentiary trail and privacy-preserving collaboration, the framework offers a practical mechanism to deter violations and support judicial verification, with potential applicability to diverse online platforms. The work advances data-right protection by integrating legal concepts with decentralized, cryptographic tooling for post-acquisition governance.

Abstract

In order to provide personalized services to users, Internet-based platforms collect and utilize user-generated behavioral data. Although the 'stop using my data' right should be a fundamental data right, which allows individuals to request their personal data to be no longer utilized by online platforms, the existing preventive data protection measures (e.g., cryptographic data elimination, differential privacy) are unfortunately not applicable. This work aims to develop the first Evidence Generation Framework for deterring post-acquisition data right violations. We formulated the 'stop using my data' problem, which captures a vantage facet of the multi-faceted notion of 'right to be forgotten'. We designed and implemented the first blockchain-assisted system to generate evidence for deterring the violations of the 'stop using my data' right. Our system employs a novel two-stage evidence generation protocol whose efficacy is ensured by a newly proposed Lemma. To validate our framework, we conducted a case study on recommendation systems with systematic evaluation experiments using two real-world datasets: the measured success rate exceeds 99%.

Paper Structure

This paper contains 35 sections, 1 theorem, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background
  3. The Right to Be Forgotten
  4. Blockchains
  5. Multi-party Computation
  6. Web3 Communities
  7. The "Stop Using My Data" Problem
  8. Platform Model
  9. Threat Model
  10. Judge Model
  11. Challenges
  12. Framework Design
  13. Overview
  14. Multi-party Computation Group
  15. Evidence Generation System
  16. ...and 20 more sections

Key Result

Lemma 6.1

Let's denote the (set of) disclosed recommendations for a probing item $p$ as set B. Let's denote the per-item-cluster of $p$ as set A. If the cardinality of $A-B$ exceeds the total number of undisclosed recommendations, it can be concluded that the platform continues utilizing the victim user's pur

Figures (6)

  • Figure 1: The problem.
  • Figure 2: The evidence generation framework.
  • Figure 3: The evidence generation dataflow.
  • Figure 4: Amazon Magazine dataset Sparsity.
  • Figure 5: Amazon Beauty dataset Sparsity.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Definition 3.1: Stop Using My Data Right
  • Definition 6.1: per-item-cluster
  • Definition 6.2: Purchase History Cluster
  • Lemma 6.1
  • Remark 1