B-Privacy: Defining and Enforcing Privacy in Weighted Voting

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. We show experimentally across 2,503 proposals in 27 Decentralized Autonomous Organization (DAOs) that, with minimal transparency degradation, our mechanism raises B-privacy by a geometric mean factor of 3.5×.

Our work offers the first principled, practical, systemic guidance for weighted-voting systems, complementing existing approaches that focus on ballot secrecy.