- DARSAN: A Decentralized Review System Suitable for NFT MarketplacesSulyab Thottungal Valapu, Tamoghna Sarkar, Jared Coleman, and 5 more authorsIn Blockchain – ICBC 2023, 2023
We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called “experts”) who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a “pre-listing” phase where only experts can review the product, and a “pre-sale” phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.
- A Survey of Probabilistic Micropayment SchemesSulyab Thottungal Valapu, and Bhaskar KrishnamachariMar 2022
Although the earliest electronic micropayment schemes date back to the mid-90s, recent years have witnessed a resurgence of research interest in the field due to the rising popularity of cryptocurrencies and the associated increase in transaction fees. Probabilistic micropayment schemes have shown particular theoretical promise due to their ability to aggregate payments beyond client-merchant pairs. In this paper, we review various probabilistic micropayment protocols proposed in both pre-cryptocurrency and post-cryptocurrency eras and provide an analysis of what the future of research in this field could look like.