An Efficient and Sybil Attack Resistant Voting Mechanism
Jeremias Lenzi

TL;DR
This paper introduces a novel voting mechanism that is both Sybil attack resistant and efficient, using Bayesian design principles to ensure robustness and optimality in decentralized decision-making.
Contribution
It proposes a new wealth-deposit based voting mechanism that prevents benefits from Sybil attacks while maximizing social welfare, a combination not achieved by existing methods.
Findings
The mechanism is SA-proof under risk-neutrality and private preferences.
It ensures efficiency by maximizing total valuation.
Players' utility is slightly reduced by multi-account voting, deterring Sybil attacks.
Abstract
Voting mechanisms are widely accepted and used methods for decentralized decision-making. Ensuring the acceptance of the voting mechanism's outcome is a crucial characteristic of robust voting systems. Consider this scenario: A group of individuals wants to choose an option from a set of alternatives without requiring an identification or proof-of-personhood system. Moreover, they want to implement utilitarianism as their selection criteria. In such a case, players could submit votes multiple times using dummy accounts, commonly known as a Sybil attack (SA), which presents a challenge for decentralized organizations. Is there a voting mechanism that always prevents players from benefiting by casting votes multiple times (SA-proof) while also selecting the alternative that maximizes the added valuations of all players (efficient)? One-person-one-vote is neither SA-proof nor efficient.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection · Spam and Phishing Detection
MethodsSparse Evolutionary Training
