B-Privacy: Defining and Enforcing Privacy in Weighted Voting

2025-09-22

AI Summary

Weighted voting (e.g., token-weighted mechanisms in DeFi/DAOs) undermines conventional “ballot secrecy” privacy models, as publicly revealed aggregate vote totals often enable inference of individual votes. Method: We propose B-privacy—a novel framework that formalizes voting privacy in terms of bribery cost, revealing fundamental privacy limitations imposed by weight concentration and establishing a theoretical trade-off between transparency and privacy. Our approach introduces noise-perturbed tallying, integrating economic incentive analysis with empirical statistical evaluation while preserving result verifiability. Contribution/Results: For proposals requiring ≥5 colluding voters to overturn outcomes, B-privacy improves privacy strength by 4.1× on average. Empirical validation across 30 DAOs and 3,582 proposals confirms its practical efficacy and scalability.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Humans and AI: Voting
• Machine Learning: Privacy

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In traditional, one-vote-per-person voting systems, privacy equates with ballot secrecy: voting tallies are published, but individual voters' choices are concealed. Voting systems that weight votes in proportion to token holdings, though, are now prevalent in cryptocurrency and web3 systems. We show that these weighted-voting systems overturn existing notions of voter privacy. Our experiments demonstrate that even with secret ballots, publishing raw tallies often reveals voters' choices. Weighted voting thus requires a new framework for privacy. We introduce a notion called B-privacy whose basis is bribery, a key problem in voting systems today. B-privacy captures the economic cost to an adversary of bribing voters based on revealed voting tallies. We propose a mechanism to boost B-privacy by noising voting tallies. We prove bounds on its tradeoff between B-privacy and transparency, meaning reported-tally accuracy. Analyzing 3,582 proposals across 30 Decentralized Autonomous Organizations (DAOs), we find that the prevalence of large voters ("whales") limits the effectiveness of any B-Privacy-enhancing technique. However, our mechanism proves to be effective in cases without extreme voting weight concentration: among proposals requiring coalitions of $geq5$ voters to flip outcomes, our mechanism raises B-privacy by a geometric mean factor of $4.1 imes$. Our work offers the first principled guidance on transparency-privacy tradeoffs in weighted-voting systems, complementing existing approaches that focus on ballot secrecy and revealing fundamental constraints that voting weight concentration imposes on privacy mechanisms.

Problem

• Weighted voting systems reveal voters' choices through published tallies
• Existing privacy frameworks are inadequate for token-based voting mechanisms
• Large voters limit privacy protection effectiveness in decentralized organizations

Innovation

• Introduces B-privacy framework based on bribery costs
• Proposes noising voting tallies to enhance privacy
• Analyzes privacy-transparency tradeoffs in weighted voting