Blockchain Papers

Anam Yousaf Nisar Ahmed Memon

No abstract is available for this record.

KrishnaBhargavi Yerraganti, D. Balasubrahmanyam, G. Venkat Vamsi, Raja Bhaiya Rajbhar · 5 authors

Centralized traditional social media platforms are controlled by entities that monetize user data, restrict content visibility, and erode privacy. In contrast, current blockchain-based social media platforms are decentralized but inefficient in moderating content, resulting in the proliferation of misinformation, objectionable content, and security threats. This paper aims to bridge this gap by developing a decentralized social media platform that ensures user privacy, content moderation, and scalability without relying on centralized control. To achieve this, the system takes advantage of IPFS for decentralized file collection and Ethereum-compatible smart contracts for authentication and content verification. A major innovation of this platform is the use of an on-device-operated Natural Language Processing (NLP) model for material filtering and moderation at the user level, ensuring that no user data is collected or centrally processed. The platform will support safe text messaging, image sharing (public/private accounts), and an explore page for the discovery of public content, while all users give full control over their data. The expected results are a scalable, censorship-resistant and privacy-centric social media network, where users maintain their content ownership while AI ensures a safe digital environment.

Matthew Verschoor, Chunyang Li, Wei Cai, Yan Bai

To our knowledge, this paper is the first to introduce an iterative semantic-clustering framework driven by Large Language Models (LLMs) for refined user profiling on Uniswap V4. Traditional numerical techniques such as K-Means falter in high-dimensional feature spaces and identify only three coarse user groups. In contrast, our LLM-based pipeline repeatedly refines clustering criteria through natural-language reasoning, producing 21 semantically interpretable sub-clusters. These fine-grained clusters can be hierarchically aggregated into three macro categories that align with the K-Means result, thereby preserving global consistency while revealing nuanced behavioral motivations and patterns. The proposed framework offers both a theoretical perspective and practical toolkit for decentralized-finance user analysis, opening new avenues for understanding on-chain behavior.

Vaishali Savale, Dhiraj Pawar, Parth Shethji, Mayur Patil

The internet is evolving from Web2's centralized model, dominated by a few tech giants, to Web3's decentralized future. Web3 dismantles walled gardens, distributing data and applications across a peer-to-peer network. Imagine information not on a single server, but replicated across countless computers. This fosters transparency and eliminates censorship. Users, not platforms, own their data. Web3 empowers creators - artists can issue tokens tied to their work, allowing fans to directly support them and even own a piece. Tokens act as the fuel for a new economy, rewarding users for contributing to projects and fostering a more collaborative online experience. This shift towards decentralization has the potential to create a more equitable and user-centric internet, one where power lies not with corporations, but with the users themselves.

Arthur Carvalho, Chad Anderson, Liudmila Zavolokina

Information systems (IS) conferences, as venues for the introduction of new knowledge to the IS community, require effective peer review systems to evaluate submitted research for quality, validity, and originality. We argue in this paper that questionable practices and degrading review quality may arise without direct incentives beyond reviewer altruism to engage in the peer review process. In particular, we highlight potential issues with arguably common practices in some IS conferences, such as peer review invitations sent to researchers who have also submitted papers for publication consideration and the increasing number of reviews performed by graduate students. To address these issues, we suggest three solutions: 1) quid pro quo rules; 2) the use of incentive-compatible methods whose scores are linked to relevant rewards; and 3) the use of blockchain-based tokens in tandem with smart contracts and zero-knowledge proofs. We conclude by offering directions the IS community can take to further study the highlighted issues and implement the proposed solutions.

Yuxuan Lu, Yuqing Kong

Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small.

Hossein Hassani, Roozbeh Razavi‐Far, Mehrdad Saif, Enrique Herrera‐Viedma

To manage consensus in opinion dynamics models (ODMs), removing bias from agents' interactions and considering their willingness are critical. It can be accomplished by providing a secure mechanism that does not disclose agents' identities and opinions in their interactions, eliminating the impact of opinion similarity on trust building. To build trust and consensus opinion, we propose a linguistic ODM based on the Blockchain technology. This model allows agents' opinions to be expressed usingZ-numbers, as opposed to regular ODMs with numerical opinions. Agents are encouraged to modify their initial opinions in response to a minimum cost consensus model. Willingness of agents to accept or refuse the suggested modifications is realized through a Blockchain regime to avoid bias. The regime, however, must be supported by a trust-building mechanism to persuade agents to alter their opinions. To this end, we propose a Blockchain-enabled trust-building mechanism to improve agents' trust and guide them toward a consensus opinion. Following a sensitivity analysis of the underlying assumptions in the developed model, the proposed ODM is tested for its efficiency and validity.

Dong-Hoon Choi, Tae-Sul Seo

In order to create a transparent and sound academic communication ecosystem centered on researchers, we developed a system that applied blockchain technology to an open peer review system. In this study, an open peer review system was developed based on Hyperledger Fabric, which is a private blockchain. The system can be operated in connection with the reviewer recommendation module of the existing submission management system. In the reviewer recommendation module, reviewers are recommended by excluding co-authors and colleagues after an expertise test. The blockchain system performs an open peer review process based on smart contracts, while the submission management system selects reviewers for peer review. A service broker intervenes between these two systems for data interchange. The system developed herein is expected to be used as a researcher-centered scholarly communication model in the open science era, in which the intervention of publishers is minimized, and authors and reviewers (as researchers) are centered.

Haoxuan Li, Hui Huang, Shichong Tan, Ning Zhang · 6 authors

Reputation evaluation system, as the publisher and analyser of evaluation, is an important influencing factor for users and sellers in online business. Traditional reputation evaluation system always requires a third party to achieve the operation of analysis and publishment. However, a third party often exposes the identity of the users, and leaks the information. As far as we know, all existing revocable reputation evaluations are based on the third-party model. In this paper, we present a new reputation evaluation system based on blockchain. Compared with traditional reputation evaluation systems, our system removes the third party, it allows users to modify their own evaluation information. Moreover, the user's privacy also can be protected. The experiment performance demonstrates that the overhead of the system is acceptable, the system is feasible and efficient.