The increasing ability of internet-connected daily life electronic gadgets has propelled smart homes into a global trend. The Internet of Things (IoT) enables ambient devices to communicate and interact seamlessly through various sensors. Emerging technical concepts like Web3 and Industry 5.0 require decentralised and intelligent systems near the network's edge. Petabytes of IoT sensor-generated data cause a shortage of storage on the Cloud servers, adding a delay factor to the IoT system. Standard cloud-based IoT systems can't fully function in areas with unstable internet. This paper addresses these challenges and proposes a solution to integrate edge computing concepts. The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. The designed prototype includes a fire and smoke detection system with MQ2 gas, dust, temperature, and flame sensors. The SVM and these sensors form a data fusion module integrating with Network Mapper (NMAP), Message Queuing Telemetry Transport (MQTT) broker, MariaDB SQL server, and InfluxDB time series database. The experiments demonstrate a fundamental edge operation with a latency of 2.45 ms (milliseconds), while NMAP integration ensures data security and device verification for sensor data storage. The synthetic simulations show positive outcomes for the data fusion-based monitoring system, where alerts are promptly triggered as sensor values change, with an overall system latency of approximately 24 ms. The developed system manages home automation, real-time monitoring for fire, smoke, gas leaks, network scans, anomaly detection, appliance usage tracking, and cloud data backup. A multi-level alert system ensures early threat mitigation, with alarms, SMS, notifications, and email alerts to maximize awareness.
This paper addresses a misalignment between market motivations and community needs in Web3, arguing that comparative ethnographic work can identify strategic opportunities in the industry. Comparing findings across different design research projects, we show how Web3 builders and founders are driving a plurality of projects and products and enabling cross‐chain operability to build diverse blockchain‐based ecosystems. Although this plurality is generative for innovation in the industry, it de‐prioritizes fully fleshed out, end‐to‐end Web3 solutions to urgent social problems, and is increasingly displacing community‐centered perspectives. We found that users desire a different kind of plurality—interoperable financial instruments across traditional banking and blockchain ‐ based platforms.
Zheng Che, Meng Shen, Zhehui Tan, Hanbiao Du · 9 authors
With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, we first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method.
Qinan Nazratulya Shafa, Hasbullah Hasbullah, I Nyoman Yoga Sumadewa
The development of online games is in line with advances in computer and network technology, including Web3 which includes the use of Blockchain, smart contracts and decentralization. One company operating in this field is Zabava Labs, located in Singapore, with their flagship product, Sentinel Haven. Although Zabava Labs is active on social media, especially Instagram, the visual image of their feed does not represent the company effectively, which results in low audience engagement and interest. This research aims to design a character mascot that is attractive to young gamers in Indonesia to improve the image and interaction of Zabava Labs on social media. The designed mascot is expected to be able to build the company brand by providing a lasting impression, facilitating effective communication with the audience, and increasing brand recognition in the market. Through a creative and innovative design approach, this mascot will become a visual element that is not only attractive, but also reflects a strong and memorable brand.
Blockchains are usually managed by blockchain nodes, which maintain a copy of all the blockchain's data and participate in validating transactions and reaching consensus with other blockchain nodes. However, running a blockchain node on your own is not easy due to the high maintenance costs and specialized hardware needed. Blockchain-as-a-service has been introduced recently by cloud giants to enable enterprises to manage blockchain nodes and networks by abstracting infrastructure setup complexities. While current BaaS solutions simplify integration and development, they suffer from inefficiencies due to fixed resources, scalability challenges, and cost inefficiencies. The purpose of this article is to analyze the integration of blockchain technology with cloud computing. In particular, we identify the costs, performance, scalability, and other challenges relating to blockchain-as-a-service. As part of our proposal, we suggest dynamic resource allocation, optimizing node computation to match web3 application requirements, and improving blockchain node scalability. The real-time adaptability of this approach ensures cost efficiency and performance improvements as workload changes. Finally, we provide research directions relevant to future research that will be required to fully utilize blockchain and cloud technology.
Digital platforms dominate our economy Without a doubt, platform business models have revolutionized almost every industry, from e-commerce (Amazon) and operating systems (iOS and Android) to transportation (Uber), film (Netflix) and hospitality (Airbnb).In 2023, four out of the five most valuable companies worldwide operated based on platform business models.Often, these platform business models have made services more accessible and significantly reduced costs for their users.Platform business models enable the platform provider, as the intermediary, to make these improvements at low costs for their users as network effects lock in users and allow the provider to collect and monetize their data.This mechanism often leads to one strong player dominating the market, allowing them to monetize their monopoly-like position.The recent upsurge in artificial intelligence (AI) has fostered fears that these platform businesses might become even more powerful.More than ever, critics are concerned that current regulations fail to mitigate these dynamics, as antitrust regulations have failed to prevent platform providers from acquiring even more market power.Regulators are often fighting an uphill battle as the platform businesses can often rely on much deeper pockets and smart lawyers who find new ways to play down their employers' real power.
Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya Prof. Sheetal Laroiya
Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity
Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.
In the last few years, art market participants have been forced to adapt to a new environment that many considered as hostile. Their technological backwardness became a major threat during the COVID crisis and its lockdowns. Since 2020, global art market players are finally trying to seize opportunities offered by new technologies.
У статті розглянуто концептуальні основи впровадження RWA-токенізації як закономірного етапу еволюції веб-технологій до децентралізованої архітектури інтернету – Web 3.0; висвітлено еволюцію інтернету від Web1 до Web3; запропоновано концепцію RWA-токенізації; представлено структурно-компонентну модель дизайну RWA-токену; виокремлено переваги смарт-контрактів для RWA-токенізації, розглянуто хронологію розвитку токенізації активів; наведено схему процесу RWA-токенізації, що демонструє, як реальні активи перетворюються на цифрові токени, відкриваючи нові горизонти для інвестицій та фінансових інновацій. Встановлено, що в контексті цифрової трансформації економіки України, особливо у повоєнний період, RWA-токенізація може стати потужним інструментом, який здатний сприяти відновленню економічного потенціалу держави завдяки залученню інвестицій, трансформації традиційних економічних моделей та забезпеченню прозорості інвестиційних процесів.
The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.
Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. As a reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use. • We propose the skip index, a data structure for efficient blockchain data retrieval. • The skip index provides guarantees about the integrity of query results. • We devise efficient algorithms to construct and query skip indices. • Compared to a sequential scan, skip indices offer a constant multiplicative speedup. • Skip indices experimentally provide a speedup of up to two orders of magnitude.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Random numbers play a vital role in many decentralized applications (dApps), such as gaming and decentralized finance (DeFi) applications. Existing random number provision mechanisms can be roughly divided into two categories, on-chain, and off-chain. On-chain approaches usually rely on the blockchain as the major input and all computations are done by blockchain nodes. The major risk for this type of method is that the input itself is susceptible to the adversary's influence. Off-chain approaches, as the name suggested, complete the generation without the involvement of blockchain nodes and share the result directly with a dApp. These mechanisms usually have a strong security assumption and high complexity. To mitigate these limitations and provide a framework that allows a dApp to balance different factors involved in random number generation, we propose a hybrid random number generation solution that leverages IoT devices equipped with trusted execution environment (TEE) as the randomness sources, and then utilizes a set of cryptographic tools to aggregate the multiple sources and obtain the final random number that can be consumed by the dApp. The new approach only needs one honest random source to guarantee the unbiasedness of the final random number and a user can configure the system to tolerate malicious participants who can refuse to respond to avoid unfavored results. We also provide a concrete construction that can further reduce the on-chain computation complexity to lower the cost of the solution in practice. We evaluate the computation and gas costs to demonstrate the effectiveness of the improvement.
Open access
2 source records
Peer-to-Peer Network Technologies
Advanced Steganography and Watermarking Techniques
By offering fresh approaches to solve long-standing issues like too much centralisation, not involving everyone, and not giving consumers enough autonomy, Web3 technologies are rapidly altering the way typical banking systems operate. One of the most fascinating fresh developments in the field is the growing Web3 membership. These empower consumers over their financial transactions by use of distributed platforms and blockchain technology. Unlike conventional membership models, Web3 payments let users to handle their own data, create explicit arrangements, and get services free from intermediaries. This article examines how Web3 subscriptions can transform the financial climate and open everyone's access. Subscription-based services let users freely access enhanced privacy, security, and control over their personal financial data. Web3 contracts remove obstacles to access so that those without bank accounts may utilize financial services, hence fostering financial inclusion. By shifting authority from centralized financial institutions to individuals, the decentralised framework of these models also allows consumers greater autonomy and independence. This paper investigates the advantages and drawbacks of Web3 accounts including legal concerns, user behavior problems, and growth capability. Following extensive research, we find methods to enhance Web3 membership systems so that they remain scalable, secure, and open for a broad spectrum of users. Finally, this research reveals how Web3 payments could alter the dynamics of financial models, therefore promoting a more open and user-centric attitude to financial services.
Open access
2 source records
FinTech, Crowdfunding, Digital Finance
Peer-to-Peer Network Technologies
Library Collection Development and Digital Resources
As a new paradigm in the Web3 era, Decentralized Autonomous Organizations (DAOs) not only embodie the spirit of decentralization and collective governance, but also are the forefront of promoting community-led innovation. However, DAOs face challenges in sustainable growth and scalability in community governance, which are closely related to the income distribution model and member participation. Therefore, the Artificial systems, Computational experiments, Parallel execution (ACP) approach is applied to optimize the contribution evaluation and incentive feedback of member behavior through parallel governance and decision-making methods, so as to improve the intelligence of the DAO incentive mechanism. On this basis, the long short-term memory network (LSTM) and combinatorial game theory are used to conduct experimental verification on information sharing within the community. The experimental results show that our proposed method can not only achieve a high degree of information sharing in the community faster than other methods, but also has the ability of autonomous dynamic adjustment. It is of great significance and value to the community governance research and scenario implementation of DAOs.
Smart contracts, self-executing agreements directly encoded in code, are fundamental to blockchain technology, especially in decentralized finance (DeFi) and Web3. However, the rise of Ponzi schemes in smart contracts poses significant risks, leading to substantial financial losses and eroding trust in blockchain systems. Existing detection methods, such as PonziGuard, depend on large amounts of labeled data and struggle to identify unseen Ponzi schemes, limiting their reliability and generalizability. In contrast, we introduce PonziSleuth, the first LLM-driven approach for detecting Ponzi smart contracts, which requires no labeled training data. PonziSleuth utilizes advanced language understanding capabilities of LLMs to analyze smart contract source code through a novel two-step zero-shot chain-of-thought prompting technique. Our extensive evaluation on benchmark datasets and real-world contracts demonstrates that PonziSleuth delivers comparable, and often superior, performance without the extensive data requirements, achieving a balanced detection accuracy of 96.06% with GPT-3.5-turbo, 93.91% with LLAMA3, and 94.27% with Mistral. In real-world detection, PonziSleuth successfully identified 15 new Ponzi schemes from 4,597 contracts verified by Etherscan in March 2024, with a false negative rate of 0% and a false positive rate of 0.29%. These results highlight PonziSleuth's capability to detect diverse and novel Ponzi schemes, marking a significant advancement in leveraging LLMs for enhancing blockchain security and mitigating financial scams.
This report introduces the Grant Maturity Index (GMI), a novel evaluative framework designed to assess the maturity and operational effectiveness of Web3 grant programs. As Web3 continues to develop, the decentralized nature of these programs brings both opportunities and challenges, particularly when it comes to governance, transparency, and community engagement. Traditional funding models are often governed by standardized processes, but Web3 grants lack such consistency, making it difficult for grant operators to measure the long-term success of their programs.The Grant Maturity Index (GMI) was created through exploratory applied research to address this gap. Inspired by the World Bank's GovTech Maturity Index (GTMI), the GMI is tailored specifically for the decentralized Web3 ecosystem. The GMI evaluates key dimensions of grant programs governance, transparency, operational efficiency, and community engagement, providing grant operators with a clear benchmark for assessing and improving their programs. The primary objectives of this research are to, first, identify the structural indicators that adequately describe Web3 grant programs. Second, to describe optimal outcomes for programs by evaluating their maturity across key operational areas. The GMI is applied to four major Ethereum Layer 2 grant programs, namely Arbitrum, Mantle, Taiko Labs, and Optimism. These case studies highlight areas where Web3 grant programs require improvement, particularly in standardizing processes, enhancing transparency, and increasing community participation.
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams.
Shraddha Vasant Prasad, D. Pushparaj Shetty, B. R. Shankar
The increasing adoption of Blockchain technology, spurred by the success of cryptocurrencies, has gained substantial traction across various sectors. A notable application of Blockchain technology is in electronic voting (e-voting), where decentralized nodes enhance the security and integrity of the voting process. Traditional voting methods suffer from shortcomings such as result delays, susceptibility to tampering, hijacking and destruction of voting machines. Given the scalability challenges of blockchains, a single blockchain network cannot feasibly cover all constituencies in a country. Therefore, a more effective approach is to implement multiple smaller independent blockchain networks, with each constituency having its own network and blockchain. This paper discusses the concept of one network and one blockchain for one constituency, which can be replicated for every other constituencies in the country to scale up. It explores a web3-based e-voting system utilizing private Ethereum blockchain technology, focusing on a network architecture and the design that features a DApp (Decentralized Application) application with a user-friendly interface for voting in polling booths and a governing Smart Contract. Voters can cast their votes using unique identifiers like Aadhaar or UID credentials. The outcomes of this proposed e-voting system demonstrate promising and viable performance for governmental elections. However, it is advisable to conduct trials in local elections or general body elections within institutions to validate its efficacy and reliability before wider adoption in larger democratic elections.
This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.
Ethereum, as a representative of Web3, adopts a novel framework called Proposer Builder Separation (PBS) to prevent the centralization of block profits in the hands of institutional Ethereum stakers. Introducing builders to generate blocks based on public transactions, PBS aims to ensure that block profits are distributed among all stakers. Through the auction among builders, only one will win the block in each slot. Ideally, the equilibrium strategy of builders under public information would lead them to bid all block profits. However, builders are now capable of extracting profits from private order flows. In this paper, we explore the effect of PBS with private order flows. Specifically, we propose the asymmetry auction model of MEV-Boost auction. Moreover, we conduct empirical study on Ethereum blocks from January 2023 to May 2024. Our analysis indicates that private order flows contribute to 54.59% of the block value, indicating that different builders will build blocks with different valuations. Interestingly, we find that builders with more private order flows (i.e., higher block valuations) are more likely to win the block, while retain larger proportion of profits. In return, such builders will further attract more private order flows, resulting in a monopolistic market gradually. Our findings reveal that PBS in current stage is unable to balance the profit distribution, which just transits the centralization of block profits from institutional stakers to the monopolistic builder.
Nikhil Vanjani, Pratik Soni, Sri Aravinda Krishnan Thyagarajan
In scenarios where a seller holds sensitive data $x$, like patient records, and a buyer seeks to obtain an evaluation of a function $f$ on $x$, solutions in trustless environments like blockchain fall into two categories: (1) Smart contract-powered solutions and (2) cryptographic solutions using tools such as adaptor signatures. The former offers atomic transactions where the buyer learns $f(x)$ upon payment. However, this approach is inefficient, costly, lacks privacy for the seller's data, and is incompatible with blockchains such as bitcoin. In contrast, the adaptor signature-based approach addresses all of the above issues but comes with an "all-or-nothing" guarantee, where the buyer fully extracts $x$ and does not support extracting $f(x)$. In this work, we bridge the gap between these approaches, developing a solution that enables fair functional sales while offering all the above properties like adaptor signatures. Towards this, we propose functional adaptor signatures (FAS), a novel cryptographic primitive and show how it can be used to enable functional sales. We formalize the security properties of FAS, among which is a new notion called witness privacy to capture seller's privacy, which ensures the buyer does not learn anything beyond $f(x)$. We present multiple variants of witness privacy, namely, witness hiding, witness indistinguishability, and zero-knowledge. We introduce two efficient constructions of FAS supporting linear functions based on groups of prime-order and lattices, that satisfy the strongest notion of witness privacy. A central conceptual contribution of our work lies in revealing a surprising connection between functional encryption and adaptor signatures. We implement our FAS construction for Schnorr signatures and show that for reasonably sized seller witnesses, all operations are quite efficient even for commodity hardware.