In museums, heritage, and non-profit cultural organisations, thought leadership on the ethical implications of AI is gathering speed. Notable initiatives include the Network of European Museum Organisations (NEMO)'s efforts to address the uptake of AI in museums (2024) and the UK's Arts and Humanities Research Council's BRAID programme dedicated to integrating Arts and Humanities research into the 'Responsible AI' ecosystem (2022). This is a fast-evolving area with new analysis and calls to action appearing with frequency. As yet however, little attention has been given to how AI is emotionally impacting lived experiences of cultural workers as organisations seek to operationalise it. This paper highlights the need to consider such impacts, as well as the general-purpose technologies AI builds upon, such as biotechnology and a connected ecosystem of devices, on cultural workers and their practices. The convergence of these technologies signals what futurist Amy Webb calls a technology "supercycle" with far-reaching implications (Aiello, 2024).This paper does not present new empirical evidence but rather portends the need for more research on the relationship between wellbeing, AI, and work in cultural organisations. Based in the UK and the Netherlands, the authors are researcher-practitioners who explore the impact of digital technology on cultural workforces. This paper critically anticipates the implications of AI on those working on the ground in cultural organisations. Our ideas and examples draw from the UK, Europe and the United States, but we hope they speak to experiences of cultural workers across the globe.In what follows, a strategic foresight method known as the "Futures Triangle" (Inayatullah, 2023) will be deployed to consider the plausible future of AI-driven cultural work that might emerge between three pushing and pulling cornersthe past, the present, and the futureeach of which is shaping the adoption of AI in our cultural organisations. This framework helps raise awareness of the trends, drivers, and signals of which cultural organisations need to be aware to ensure an integration of AI that empowers workers to engage effectively while addressing the ethical dilemmas involved. This structure recognises the history that has shaped our current decisions, determines what to carry forward or leave behind, and confronts present challenges. We must consider the impact of our choices and how they will resonate into the future, ensuring they reflect the people and context of multiple possible futures.In Future Thinking, trends refer to long-term patterns that can be carried forward into the future. There are two trends that have impacted cultural work, wellbeing, and AI: digital transformation and decolonisation.The recent history of digital transformation in museums, heritage and cultural organisations is convoluted. Many associate momentum to a 2017 review of English museums which argued for "dynamic data for dynamic collections" (DCMS, 2017: 64). The following year, the UK's Department for Culture, Media, and Sport (DCMS) launched Culture is Digital, a report calling for "practitioners and organisations across the cultural and tech sectors" (2018: 17) to develop "digital thinking" (ibid: 9) stating that organisations felt "held back" by a lack of infrastructure, resources, digital skills and leadership training, resulting in "a fragmented approach" to technology (ibid: 5). Despite digital developments in many cultural organisations the story remained patchy, with funding tending to prioritise "shiny" short-term over long-term infrastructure projects, an ongoing deficit of digital literacy, cataloguing backlogs, and a predominance of non-interoperable systems and formats. This often led to reluctance, fear, and apathy when it came to instilling digital change up to and since the Covid-19 pandemic. As a respondent in a survey on AI in heritage organisations stated: even if AI has the potential "to revolutionise the heritage sector", there remain "real problems that are affecting us, like skills shortages, funding shortages and volunteer shortages" (Oates, 2023). They continued: "the obsession with 'digital' is unhelpful, especially when digital is so poorly defined that the adjective is arbitrarily used as a meaningless noun" (ibid). However, as John Stack, Director of Digital Innovation and Technology at The National Gallery, notes: "Unlike previous recent waves of technology (crypto currency, web3, NFTs, etc.) the AI revolution feels different -there's a sense in which it seems likely to change many things, but we don't fully understand the potential and implications" (Stack, J. (2024) Email to Sophie Frost, 9 January).Trend 2: Decolonisation Often founded on colonial principles, cultural organisations carry legacies that have both hindered and guided their embrace of new technologies. Decolonisation initiatives have burgeoned in recent years with bodies such as UNESCO, International Council of Museums (ICOM), American Alliance of Museums, and Museums Association supporting the need to "recognise the integral role of empire in museumsfrom their creation to the present day" (Museums Association, 2024). While actions for enhanced traceability and diligence in the acquisition of cultural objects has grown, there has been less vigilance regarding the integration of emerging technologies which invite new, equally problematic, forms of coded gaze into legacy institutions. Despite conversations amongst museum and heritage practitioners on the dangers of commercial technologies (Pratty, 2019), there have been few practical attempts to source long-term ethical alternatives. As Oonagh Murphy, editor of this Issue, points out: there is a need to "engage in a broader conversation about the power and impact" of digital tools and products (2024: 73). NEMO has implied that museums have the "potential" to be "partners in the development of ethical practices related to emerging technologies" ( 2024), but there is more work to be done to link decolonisation efforts with the integration of AI.Drivers are broad long-term forces that are likely to have a significant impact on the future. Drawing on research exploring AI in the global workplace, we locate the following as shaping how we imagine the signals to emerge in the future.Research suggests that AI will predominantly result in augmentation rather than automation (International Labour Organization, 2024: 6). Clerical work will be most exposed to automation because of generative AI, with 24% of tasks "highly exposed" (ibid). This is a gendered issue. As women tend to be overrepresented in the clerical field, women's jobs may be "twice as likely to be exposed" as men's (Muldoon et al., 2024: 53). Museum and heritage environments have long been recognised as "pink collar" workplaces (GEMM, 2019) whereby women undertake most administrative roles. In the USA, 58.6% of the workforce is female across archival, curatorial and museum technician roles (DataUSA, 2022) while a UK report on the arts, culture and heritage workforce found only 34% of women occupy managerial or director positions with the majority in junior roles (PEC, 2024). Jobs in areas such as collections management could have large components of their roles augmented by AI. Large Language Models excel at "formal, standardised tasks with clear objectives and large amounts of text data" (Muldoon et al. 2024: 53). In cultural organisations, this form of digital data entry is typically done by women on relatively low pay (Frost, 2022). Generative AI is also valuable in content creation, meaning that marketing and social media roles may be affected.Driver 2: Demand for AI Literacy Discussions of AI workplace integration emphasise reskilling workforces to support its effective use. In the context of cultural organisations, stakeholders are recognising AI literacy as an opportunity to reshape digital work, introduce new efficiencies, whilst emphasise the value of employee agency to digitally experiment through AI methods. Industry specialist Jocelyn Burnham, who offers AI workshops for the cultural and creative sectors, recognises AI as a tool for experimenting with what new technologies might offer, rather than being anxious about how they might disrupt the status quo. AI prompts us to ask more critically engaged questions, to understand our own needs and the needs of audiences in different ways. Large Language Models (LLMs) are helping workforces augment their digital skills, enabling those who have not been able to use new technologies so easily in the past to do so, whilst helping staff learn in different ways (Burnham, J. (2024) Interview with Sophie Frost, 17 January 2024).There is a flipside. The International Labour Organization notes that some parts of the world are at risk of an "AI divide" whereby "high income nations disproportionately benefit from AI advancements" (2024: 5). This could be the case in the cultural field also where AI training is inconsistent. As Angie Judge, CEO of Dexibit, notes: "AI will quickly create a world of the haves and have nots. I hope the museum sector will find itself on the right side of that equation" (Styx, 2024). For Stack, "best practice is still emerging and much of the work is 'bottom up' with museum practitioners exploring the potential, rather than top down with managers and leaders directing this work". He continues: "museums are starting to recognise the need for a policy document that is set for internal review every six months or so, while other internal museum policies often go years before review" (Stack, J. ( 2024) Email to Sophie Frost, 9 January). Such need for agility in the creation of AI policy has been recognised by the UK government also: their Generative AI Framework for HMG describes itself as "necessarily incomplete and dynamic" (Gov.uk, 2024).The most consequential driver for cultural organisations may be the inherent bias embedded within and potential misuse of LLM training data. Many have documented the ways AI datasets have been drawn through the rules and algorithms of those who trained them, leading to race and gender discrimination across multiple online platforms (Leavy et. al., 2020;Buolamwini, 2023). Much of this software has been invested in by legacy tech companies and it is widely recognised that technological development is under the control of those "with strong imperatives to continue expanding their operations and increasing their profits" (Muldoon et al. 2024: 161). New government guidelines for the public sector emphasise the need, when working with AI, "to establish and communicate how you will address ethical concerns from the start" (Gov.uk, 2024) but how can smaller, less powerful organisations reclaim power when limited by the options available to them? Helpfully, the European Commission's recent foresight report focused on the future of Big Tech in Europe and its implications for research and innovation (R&I). Using a scenario-based approach, it offers recommendations to guide the EU's R&I policy. Significantly, all four imagined scenarios indicated "varieties of high tech capitalism" with only one emphasising the prosperity of civil society over Big Tech (2024: 8). Cultural organisations can use such resources to understand the current landscape and strategically respond not only to present developments but anticipate alternative futures of information control.There is an additionally troubling aspect regarding the increased risk of copyright infringement for cultural organisations who make income from their picture libraries through licensing for reproduction and commercial research. This issue gained attention in the UK through high-profile publications such as the House of Commons Culture, Media and Sport Committee report on Content Remuneration, which calls on government "to ensure that creators have proper mechanisms to enforce their content and receive fair compensation when their works are used by AI systems" (House of Commons, 2024). If this driver is not managed proactively, what might be its effects on income in years to come?The exponential growth of AI-related employment has surfaced questions regarding its hidden human costs (Muldoon, Graham & Cant, 2024;Gray & Suri, 2022;Catanzariti et al., 2021). Attention is being paid to how human labour plays a pivotal role in enabling AI technology, particularly generative AI, through the paid, piecework of collecting, processing, and labelling of datasets needed by models such as ChatGPT for training. Our research has observed the emotional toll of digitally driven labour in museums, heritage, and cultural organisations (Frost, 2021 and2022;Vargas, 2020). Daily tasks such as establishing suitable naming conventions, cleaning up and figuring out problems in the data, and dealing with acquisition backlogs, require persistence, care, clarity, and impartiality. Those who promote digital change experience personal, psychological costs and ethical dilemmas consistent with other types of cultural and creative work (see Banks, 2017;Belfiore, 2021). When it comes to AI, the concern of many digital staff is, as Steven Franklin, Social Media Manager at The Royal Institution explains, "if you've got technology that is inherently designed to increase individuals' productivity then the demands of the job will probably go up with it. So, you're going to be expecting more people to do more" (Franklin, S. ( 2024) Interview with Sophie Frost, 12 January).A recent study explored the "dark side effects of digital working" of 142 workers, specifically their levels of stress, overload, anxiety and Fear of Missing Out on Information (Marsh et. al., 2024). It found that "employees who are overloaded by information or worried about missing out on it in the digital workplace face risks to their well-being at work" (2024: 12) and stated that greater consideration of the digital workplace "is essential to not only employee productivity but also wellbeing in modern organisations" (ibid). Cultural workplaces similarly need to reflect on how AI will impact well-being, and how the information overload of working with new technology has emotional consequences for workers.It is easy to suggest that AI poses a threat to those who have spent years honing their skills as cultural workers, drawing on data suggesting that those with higher qualifications are more exposed to AI (Department for Education 2023: 18). We propose instead that cultural roles will be positively augmented by AI, creating opportunities for better discoverability and searchability in daily operations as well as in creative reuse and reimagination within the interpretation of collections and audience engagement. AI will need to be accommodated and understood within all job roles in the cultural workplace; it will become the responsibilityand the possibility -of all (and yes, this will require consistent resourcing of time, people, energy, and moneyas does all digital maturity work).More concerning for us is a wider existential issue: if AI, as has been argued by many in the creative industries, is devaluing creativity as a specifically human endeavour, will this deprioritise the importance of the institutions that care for it? We have heard outcries, in areas such as screenwriting, music production, video games and the visual arts regarding the use of generative AI and other digital technologies to replicate human creative outputs (SAG-AFTRA, 2024;Weiss, 2023;The Art Newspaper, 2023). As the quality of generative AI develops, will there not be a moment when even experts are unable to distinguish between AI and human generated cultural artefacts? What might this mean for history itself, for the historical record? Nick Bostrom's Deep Utopia ( 2024) offers one answerhe invites us to rethink the complexity of utopia and confront the unintended consequences of our pursuit for better representation and justice, challenging the binary thinking of utopian vs. dystopian outcomes. He argues that true progress instead requires moving beyond fixed notions of perfection to consider multifaceted possibilities and risks of future realities.Signals represent small, local innovations or disruptions that have the potential to grow in scale and distribution (Howard, 2021). Below are five signals, posed as 'What if?' questions, that critically anticipate how cultural workplaces might tackle the present and reimagine a future with AI. "What if" questions encourage creative thinking, challenge assumptions, and open alternative possibilities. For futurist Peter Schwartz, they are part of scenario planning, a strategic tool to prepare for uncertainty by imagining diverse future scenarios (1991).1. What if we developed greater "coopetition" -cooperating with our competitors to achieve a common goal -as we integrate AI in our cultural workplaces? Our sector must radically cooperate with other stakeholders on the ethics of AI -from corporations to governments, from other cultural organisations to grassroots bodies.workplaces? The integration of AI in our cultural workplaces has required on-the-job learning. New technological developments mean greater demand for new digital literacies in our workplaces; it is imperative to embrace lifelong learning and the upskilling of workforces.technology, climate change, and social justice, and acknowledged more openly how they in our cultural workplaces? are trends and thinking about can reshape and of and What if we that the emotional and psychological of digital work to employment practices in our cultural workplaces? the wellbeing of cultural workers and and by recognising the human AI, cultural organisations will ensure that AI both work and public What if we the enabling AI, their impact on cultural workers, our role as cultural to the global conversation on AI in ways that reflect and lived experience in our cultural workplaces? This encourage us to the AI development and consider how cultural can their to guide practices that the future of the cultural is needed is more empirical research on how employee wellbeing and job quality in to AI innovation are being in museums, heritage and cultural more with other parts of the cultural and creative as we operationalise and more at the of leadership and -of how AI and within a of and cultural we need to learn to better and this ensuring that the integration of AI and lived experiences of cultural workers rather than
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
This paper summarises the intersection of Web3 and AI technologies, synergies between these technologies, and gaps that we suggest exist in the conception of the possible integrations of these technologies. The summary is informed by a comprehensive literature review of current academic and industry papers, analyst reports, and Ethereum research community blogposts. We focus our contribution on the perceived gaps and detail some novel approaches that would benefit the blockchain/Web3 ecosystem. We believe that the overview presented in this paper will help guide researchers interested in the intersection of Web3 and AI technologies.
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.
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.
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
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.
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.
Decentralized Exchanges (DEXs) are pivotal applications in the Decentralized Finance (DeFi) landscape, aiming to facilitate trustless cryptocurrency trading by relying on smart contracts and blockchain networks. The developments in the DEXs sector began with the implementation of an Automated Market Maker (AMM) system using a simple math formula by Uniswap in 2018. Absorbing significant funding and the attention of web3 enthusiasts, DEXs have seen numerous advancements in their evolution. A notable recent advancement is the introduction of hooks in Uniswap v4, which allows users to take advantage of a wide range of plugin-like features with liquidity pools. This paper provides a comprehensive classification and comparative analyses of prominent DEX protocols, namely Uniswap, Curve, and Balancer, in addition to investigating other protocols' noteworthy aspects. The evaluation framework encompasses mechanisms, components, mathematical formulations, and the performance of liquidity pools. The goals are to elucidate the strengths and limitations of different AMM models, highlight emerging concepts in DEX development, outline current challenges, and differentiate optimal models for specific applications. The results and comparative insights can be a reference for web3 developers, blockchain researchers, traders, and regulatory parties.
Nischal Aryal, Fariba Ghaffari, E. Bertin, Noël Crespi
The rise of internet and data usage highlights the importance of service provisioning for Mobile Network Operators (MNOs) in expanding their operations and meeting user demands. Implementing scalable and secure authentication and access control mechanisms is crucial for enabling service utilization among eligible users and ensuring the viability of emerging business models. Conventional centralized approaches face limitations such as single-point-of-failure, low scalability, computational overhead, and privacy vulnerabilities. MNOs must explore innovative business models to augment revenue streams and address these challenges. Realizing such models involves automating user contracts with service providers and safeguarding user privacy regarding data sharing with external entities. Blockchain technology offers a transformative avenue for integration within existing MNO infrastructures, providing novel distributed authentication and access control methodologies. We propose a new business model for MNOs and service providers in which an Attribute-Based Access Control (ABAC) framework handles user access to services on top of Blockchain. Moreover, a tokenized data-sharing method facilitates selective data sharing with service providers through MNO channels based on user-defined permissions. Central to this approach are non-fungible tokens (NFTs) and the Self-Sovereign Identity (SSI) paradigm, where NFTs ensure secure, decentralized tokenization of user data, and SSI empowers users with ownership and control over their data. Assessments confirm the scalability of this solution, making it suitable for different use-case requirements.
Web3, also known as the Decentralized Web, is a vision for the next generation of Web applications, supported by blockchain technology, where users have stronger ownership over their data and identity. One of the key components in Web3 is wallets, which hold a user’s public and private keys, manage digital tokens, and sign transaction details. Despite their significance in securely managing digital identities, wallets also introduce vulnerabilities that, if overlooked, can compromise users’ privacy and security, particularly during data exchange with network endpoints. We investigate how popular wallets like MetaMask can inadvertently expose sensitive information to RPC endpoints, even when no transactions are made. Additionally, we examine the confidentiality risks associated with the registration requirements of major node providers, highlighting how the collection of personal and financial details can further threaten user privacy. We briefly report on ongoing work in analyzing the characteristics of wallets across various blockchain networks to identify key security and privacy features that can be integrated into new wallet designs. This research aims to address the challenges inherent in wallet security and privacy within Web3.