
Reputation systems are fundamental to fostering trust and cooperation in digital environments, yet existing solutions often struggle with centralization, vulnerability to manipulation, and limited portability. Centralized reputation platforms can be opaque, censored and susceptible to become single points of failure, while decentralized ones face challenges, such as Sybil attacks, malicious strategies (e.g., ballot stuffing, bad-mouthing) exercised by entities of high influence, i.e. whales, and privacy concerns. This paper addresses these persistent issues by proposing a blockchain-based reputation framework that integrates robust identity verification, square root voting constraints, and dynamic stake-based incentives. Rating power is linked to the reputation of the rater that puts its reputation at stake. The model aims to ensure that reputation is earned and maintained through verifiable, community-aligned actions, while simultaneously limiting the potential for abuse by malicious actors or disproportionately influential participants. By leveraging decentralized identifiers, zero-knowledge proofs, and transparent incentive mechanisms, the proposed system seeks to balance transparency, fairness, and privacy. Extensive simulation experiments prove that the approach is effective to reveal the true quality of entities, even in presence of 49% colluding voters. The approach is designed to be adaptable across diverse domains, ranging from marketplaces and collaborative platforms to decentralized finance and governance.
blockchain, Sybil attack, trust, hidden quality, collusion
blockchain, Sybil attack, trust, hidden quality, collusion
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
