Zeineb Ben Sassi, Chiheb Chahine Yaici, Jiahui Xiang, Osman Salem · 5 authors
Zero-Knowledge Proof (ZKP) protocols offer a powerful foundation for privacy-preserving authentication by allowing one party to prove knowledge of a secret without revealing it. Such protocols are increasingly relevant in domains such as secure communications, blockchain technologies, digital identity management, and e-health, where data confidentiality and integrity are critical. While various ZKP schemes exist, their practical performance remains a key factor in choosing the appropriate protocol for real-world applications, since efficiency directly impacts scalability, user experience, and system adoption.In this work, we conduct a comparative benchmarking study of five no table ZKP-based authentication protocols: Fiat–Shamir, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK), Zero-Knowledge Scalable Transparent Argument of Knowledge, (zk-STARK), Schnorr, and Guillou–Quisquater. Each protocol is evaluated in a standardized virtualized environment to ensure fair comparisons across implementations. We measure and analyze multiple performance metrics, including prover and verifier execution time, Central Processing Unit (CPU) and memory consumption, and network usage per proof. Our results reveal significant differences in resource efficiency, highlighting trade-offs between computational cost, proof size, and cryptographic expressiveness.This study provides a systematic evaluation clarifying the relative strengths and weaknesses of widely used ZKP protocols, serving as a practical reference for researchers, practitioners, and system designers seeking to integrate zero-knowledge techniques under real-world performance constraints.
Kriti Patidar, Swapnil Jain, Mohammad Husain, Mohd Muqeem · 9 authors
As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.
S. Raghavendrachar, Gagan Shivanna, V. Aditya, H. N. Karthik · 5 authors
Financial transparency is a persistent challenge for NGOs, leading to trust issues, inefficiencies, and fund mismanagement. Trust Block is a blockchain-based web application designed to enhance accountability by enabling real-time tracking of donations and fund allocations through Ethereum blockchain, smart contracts, and decentralised ledgers. Built with React.js, Node.js, and MongoDB, and secured using Ganache and Solidity, the system automates financial processes, minimises human intervention, and ensures immutable transaction records. By providing real-time insights to donors and regulatory bodies, Trust Block fosters trust and financial integrity in the NGO sector, demonstrating the power of Web3 technologies in creating secure and transparent transactions.
In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals.
Yassine Maadallah, Younès El Bouzekri El Idrissi, Youssef Baddi
Blockchain technology offers a promising foundation for trustworthy, auditable communication in Internet of Things (IoT) networks. In such settings, the consensus protocol is the primary determinant of feasibility because devices operate under tight constraints in energy, computation, latency, and bandwidth. This paper delivers a structured comparative review of Proof of Work (PoW) and Proof of Stake (PoS) in the context of IoT. We first articulate their operating principles, security assumptions, and threat models, then evaluate trade-offs across throughput, latency, scalability, energy consumption, hardware requirements, and attack resistance. While PoW provides strong probabilistic finality and robust security under open membership, its high energy cost and mining difficulty make it poorly aligned with low-power sensor/actuator nodes. PoS and PoS-inspired variants reduce resource use and enable faster confirmation, but require careful design to mitigate concentration risks, long-range and nothing-at-stake attacks, and to preserve decentralization. Beyond these canonical schemes, we review emerging alternatives tailored to IoT, including lightweight PoW, committee-based BFT hybrids, and DAG-based ledgers that decouple consensus from transaction validation. We synthesize implementation considerations, including identity and stake management, clock synchronization, network churn, and intermittent connectivity, and outline selection guidelines for common IoT deployment patterns (edge-centric, gateway-assisted, and intermittently connected swarms). Our analysis indicates that PoS and related frameworks are generally better suited for IoT, particularly when combined with hierarchical or hybrid designs. The review concludes with open research directions and a pragmatic checklist for researchers and practitioners evaluating consensus for constrained IoT systems.
This study examines the role of interpersonal communication in disseminating airdrop information as an alternative income source within the Web3 industry, focusing on the CryptoIndo Jogja community. Airdrops, as a method of distributing digital assets without financial investment, are accessible to individuals from diverse backgrounds and depend on reliable information exchange. Using a qualitative descriptive approach, data were collected through in-depth interviews, observations, and documentation. Joseph A. DeVito’s Interpersonal Communication Theory and Ordonez’s Information Dissemination Theory were applied to analyze communication dynamics within the community. Findings show that interpersonal communication in CryptoIndo Jogja is characterized by openness, empathy, supportiveness, and equality, fostering trust and collaborative learning. Members freely share tutorials, insights, and risk considerations related to airdrops through direct discussions, Telegram groups, supported by credible communicators and timely, clear messages. The results indicate that while airdrops are not a primary source of income, they provide supplementary rewards ranging from USD 30 to 500 and improve participants’ understanding of the Web3 ecosystem. This research highlights that effective interpersonal communication, combined with strategic information dissemination, accelerates knowledge sharing, strengthens community engagement, and empowers individuals to access emerging opportunities in the decentralized digital economy. Keywords: Interpersonal Communication, Airdrop, Web3 Community, Information Dissemination
Decentralized Finance (DeFi) represents the new generation of blockchain financial services by developing an open-access financial model without banking or lending institution intermediaries. However, DeFi's open feature threatens its security, making it vulnerable and a target for different attack types. In this systematic review, we present the security of DeFi by selecting fifteen studies from 2020 to 2024 to determine and display the security solutions' effectiveness in identifying the attacks, focusing on various DeFi components such as smart contracts, DEX, AMM, governance, AMM-based DEX, and smart contracts with (DEX, Oracle); detecting different kinds of attacks (e.g., price manipulation, Oracle manipulation, flash loan) using detection tools (e.g., DeFort, CRPWarner, FORAY); we find out that 40% of the selected studies focus on Oracle manipulation attack, 33.33% for price manipulation and flash loan attacks separately, followed by 13.33% for (MEV, rug pull, front-running, Token Leakage, and deep logical bugs), 6.67% for (EEV, reentrancy, sandwich, access control, and state derailment defects). We compare the studies based on the attack type that they detected using four state-of-the-art types of research, such as DeFiScope, FlashSyn, SecPLF, and DeFiGuard; this indicates the concentration of the trend studies is on accuracy and combining AI in DeFi security, or aggregating the existing tools with it, giving an overview of DeFi components' security, underlining the gaps in the attack types that future research can address to build more robust, trustworthy, and secure DeFi systems.
The swift advancement of technology has transformed numerous sectors, particularly the financial services business, with Decentralised Finance (DeFi) emerging as a notable disruptor. To guarantee the sustained development and integration of DeFi, it is necessary to investigate and comprehend the emerging trends in this field. This study presents a bibliometric analysis of 181 Decentralized Finance articles published from 2010 to 2024 in the Scopus database The data were examined and illustrated utilising the VoS viewer platform and R software, yielding both descriptive and visual insights. Authored by 503 researchers across 418 universities, these works span 129 journals and cite 10,428 references. Publications show an annual growth rate of 29.2 %, with 2024 exhibiting unprecedented output. Frontiers in Blockchain leads with six publications, followed by the Journal of Risk and Financial Management. New Zealand tops the citation rankings, led by Auckland University with 338 citations. Bellavitis C. and Chen Y. are the most prolific authors each with 338 citations. The cluster analysis identifies six thematic areas, offering insights into various aspects of decentralized finance. This study offers critical insights for academics, policymakers, and industry practitioners by mapping DeFi's transition from conventional financial systems to decentralized ecosystems. The findings illuminate research gaps, propose future research avenues, and underscore the necessity of developing policies and cyber hygiene protocols to mitigate the risks of decentralized finance. This work thus serves as a valuable resource for advancing the discourse on DeFi and its implications for financial innovation.
Stergios Intzes, Georgios D. Papadopoulos, Lykourgos Magafas
Our analysis focuses on the stock cryptocurrency market, by studying a group of nineteen cryptocurrencies where their capitalisation is about 99% of the total market. Specifically, it is examined this group of cryptocurrencies for the period from 2017 up to 2024, taking into account the effect of COVID-19 pandemic. As far as we know, this is the first time that this kind of study has been published, where it takes place by creating various networks based on stock cryptocurrency correlation, in order to be possible to visualise and access these relationships using methods from Complex Networks. The evaluation results show that, there are three different communities within the crypto market and although COVID-19 pandemic and ongoing geopolitical changes, there is a notable trend towards an increase in the number of cryptocurrencies. Additionally, it is applied discriminant analysis to identify the differences among various cryptocurrencies based on their features.
As blockchain technology continues to evolve, the security of smart contracts has increasingly drawn attention from both academia and industry. The Move language, with its unique resource model and linear type system, provides a solid foundation for the security of digital assets. However, smart contracts still face new security challenges due to developer programming errors and the potential risks associated with cross-module interactions. This paper systematically analyzes the limitations of existing security tools within the Move ecosystem and reveals their unique vulnerability patterns. To address these issues, it introduces MoveScanner, a static analysis tool based on a control flow graph and data flow analysis architecture. By incorporating cross-module call graph tracking, MoveScanner can effectively identify five key types of security vulnerabilities, including resource leaks, weak permission management, and arithmetic overflows. In terms of design, MoveScanner adheres to a modular principle, supports bytecode-level analysis and multi-chain adaptation, and introduces innovative resource trajectory tracking algorithms and capability matrix analysis methods, thereby significantly reducing the false positive rate. Empirical results show that MoveScanner achieved 88.2% detection accuracy in benchmark testing, filling the gap in security tools in the Move ecosystem. Furthermore, this paper identifies twelve new types of security risks based on the resource-oriented programming paradigm and provides a theoretical foundation and practical experience for the development of smart contract security mechanisms. Future work will focus on combining formal verification and dynamic analysis techniques to build a security protection framework covering the entire contract lifecycle
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
Haoxiang Luo, Ruichen Zhang, Yinqiu Liu, Gang Sun · 6 authors
Low-altitude airspace is becoming a new frontier for smart city services and commerce. Networks of drones, electric Vertical Takeoff and Landing (eVTOL) vehicles, and other aircraft, termed Low-Altitude Economic Networks (LAENets), promise to transform urban logistics, aerial sensing, and communication. A key challenge is how to efficiently share and trust the computing utility, termed “computility”, of these aerial devices. We propose treating the computing power on aircraft as tokenized Real-World Assets (RWAs) that can be traded and orchestrated via blockchain. By representing distributed edge computing resources as blockchain tokens, disparate devices can form Low-Altitude Computility Networks (LACNets), collaborative computing clusters in the sky. We first compare blockchain technologies, non-fungible tokens (NFTs), and RWA frameworks to clarify how physical hardware and its computational output can be tokenized as assets. Then, we present an architecture using blockchain to integrate aircraft fleets into a secure, interoperable computing network. Furthermore, a case study models an urban logistics LACNet of delivery drones and air-taxis. Simulation results indicate improvements in task latency, trust assurance, and resource efficiency when leveraging RWA-based coordination. Finally, we discuss future research directions, including AI-driven orchestration, edge AI offloading and collaborative computing, and cross-jurisdictional policy for tokenized assets.
In the paper, we consider multilevel multitask analysis of cryptocurrency news using a fine-tuned Mistral 7B large language model with retrieval-augmented generation (RAG). On the first level of analytics, the fine-tuned model generates graph and text summaries with sentiment scores as well as JSON representations of summaries. Higher levels perform hierarchical stacking that consolidates sets of graph-based and text-based summaries as well as summaries of summaries into comprehensive reports. The combination of graph and text summaries provides complementary views of cryptocurrency news. The model is fine-tuned with 4-bit quantization using the PEFT/LoRA approach. The representation of cryptocurrency news as knowledge graph can essentially eliminate problems with large language model hallucinations. The obtained results demonstrate that the use of fine-tuned Mistral 7B LLM models for multilevel cryptocurrency news analysis can conduct informative qualitative and quantitative analytics, providing important insights.
This study investigates the impact of digital currencies (including central bank digital currencies [CBDCs], cryptocurrencies, and Ethereum) on monetary policy effectiveness, specifically focusing on inflation-targeting success and financial stability. Using Autoregressive Distributed Lag (ARDL) modelling on monthly global data spanning January 2010 to December 2024, the empirical findings demonstrate that digital currencies significantly improve monetary policy outcomes. The results indicate that CBDCs and Ethereum transactions notably enhance inflation-targeting success, enabling central banks to better achieve targeted inflation through improved transaction efficiency and transparency. Ethereum also consistently demonstrates a stabilising impact on financial stability by reducing inflation volatility. Conversely, cryptocurrencies exhibit mixed impacts, suggesting potential speculative disruptions. The error-correction mechanisms highlight robust short-run adjustments towards equilibrium, supporting the reliability of the ARDL approach. These findings emphasize the need for policymakers to strategically integrate digital currencies into monetary policy frameworks, and recommend enhanced regulatory oversight, strategic adoption of Ethereum technology, and careful management of monetary growth and velocity of money to sustain economic stability.
In a time of growing environmental issues and climate change, the drive toward sustainability is more important than ever. Startups and small businesses are expected to be more instrumental in forming a sustainable future as world economies move toward greener paradigms. For many of these businesses, though, the financial load related to sustainable infrastructure, eco-innovation, and clean technology still be a major obstacle. For sustainable businesses trying to bring environmentally friendly ideas to market without sacrificing financial viability, green financing options including grants, subsidies, and green loans provide essential lifelines. Emphasizing the need of access to specific funding resources that support environmentally friendly practices, this abstract investigates the several green financing options open to startups. Examining both public and private sector projects emphasizes how green finance closes the innovation gap with implementation, especially for early-stage businesses trying to scale their green solutions. Grants and subsidies represent among the most well-known sources of green money. Usually governments, international organizations, and environmental NGOs supply these financial support to inspire creativity in fields including waste management, green manufacturing, sustainable agriculture, and renewable energy. Grants are a great choice for startups with limited cash flow since they unlike loans do not demand repayment. Many environmental grantinitiatives to support clean tech development have been started in areas including the European Union, North America, and portions of Asia. As part of the EU's larger goal to reach net-zero emissions by 2050, the European Green Deal, for instance, provides billions in support to sustainable businesses. To lower the initial costs of green investments, numerous local and national governments also provide direct subsidies and tax breaks. These could include financing for research and development of low-carbon technologies, subsidies for fleets of electric vehicles, or rebates for solar panel installations. In addition to fostering the growth of green startups, these policies hasten the market uptake of sustainable goods and services. Green loans have become a powerful instrument for sustainable finance in addition to grants. These are loans specifically designated for environmentally beneficial projects, and they frequently have favorable conditions like reduced interest rates, extended payback periods, or repayment plans that are based on performance. To assist with climate-resilient projects, organizations such as the World Bank, the Green Climate Fund, and several green investment banks provide specialized green loan programs. In order to specifically serve small and medium-sized businesses (SMEs) with environmental missions, some commercial banks have also entered this market by introducing green loan portfolios. Accessing green loans or grants for startups in need of these funds necessitates both a strong business plan and an unambiguous proof of environmental impact. The majority of funding organizations assess applications using standards like energy efficiency, circularity, social sustainability, and carbon footprint reduction. Thus, it is essential to have solid environmental metrics and data to support assertions. Furthermore, obtaining certifications such as B-Corp status or compliance with ESG (Environmental, Social, and Governance) standards can boost one's credibility and chances of getting funding. Additionally, startups now have more opportunities to interact with mission-driven investors who value sustainability in addition to financial returns thanks to the growth of impact investing. Green-minded venture capital firms and angel investors frequently offer seed money to eco-innovative companies, seeking high-growth prospects in line with long-term environmental objectives. Additionally, by reaching out to eco-aware communities, crowdfunding websites such as Kickstarter and Indiegogo are being used to fund green startups. Notwithstanding these encouraging advancements, obstacles still exist. Many startups are not equipped with the knowledge, skills, or resources necessary to successfully negotiate the intricate world of green finance. Grant and loan application procedures may be extremely competitive and cumbersome. Additionally, global scalability is hampered by the uneven distribution of green funding across various regions. Governments, financial institutions, and the private sector must work together more closely to close these gaps in addition to implementing policy changes and raising entrepreneur financial literacy. To address these challenges, startup incubators, accelerators, and advisory organizations are increasingly offering green finance consulting services, helping early-stage companies identify suitable funding options, prepare compelling applications, and build investor-ready sustainability strategies. Digital tools and platforms are also emerging to match green startups with appropriate funding sources, thereby streamlining the connection between innovative ideas and capital. In conclusion, green financing is not merely a niche category of economic support; it is an essential enabler of the global transition toward a more sustainable economy. By making green finance more accessible, equitable, and aligned with the realities of early-stage startups, stakeholders can unlock a wave of innovation that tackles some of the world’ s most pressing environmental issues. Whether through grants, subsidies, green loans, or impact investing, the opportunities for sustainable entrepreneurship have never been more abundant, but seizing them requires a well-informed, strategic, and purpose-driven approach.
Abstract In the context of the IoT platform, the smart home represents a quintessential application scenario. Here, device-to-device (D2D) collaboration serves as the core element of its ecosystem, playing a crucial role in implementing diversified automated execution scenarios that are customized to fulfill user requirements. The progressive integration of edge computing and AI technologies has enhanced the collaboration among heterogeneous devices. Nevertheless, the conventional centralized D2D collaboration authorization decision-making supported by a single IoT Hub violates the Principle of Least Privilege (PoLP), which is a foundational design tenet that has been empirically validated as an optimal engineering practice for enhancing system security and reliability in IoT ecosystems. If there is a trade-off of PoLP violations, it fails to meet the users’ Quality of Experience (QoE). To address this issue, we propose D2DA, a distributed authorization decision-making model for smart home D2D collaboration, which constructs a distributed decision-making consensus network suitable for the edge side of smart homes by leveraging distributed ledger technology. D2DA presents a machine learning algorithm with a time complexity of O ( n ). Through this algorithm, consensus nodes can be efficiently and dynamically selected. Furthermore, D2DA ensures the security of the D2D collaboration process via wallets and hash verification. Extensive experiments conducted on a real-world smart home scenario validate that the decision-making latency of D2DA is on par with that of a single IoT Hub mode. The average latency for verifying the correctness of the newly added execution results is only 0.08% of the system time of D2DA, which is negligible.
Samuel Wesonga Usolo, Annette Okoth, David Angwenyi
Youth unemployment remains a major concern, particularly in African countries with the youngest population globally. In Kenya, youth unemployment rate has shown fluctuations despite several government efforts such as the Youth Enterprise Development Fund (YEDF), Kenya Youth Empowerment Project (KYEP) and the Youth Employment Scheme Abroad (YESA). The impact of devolution on youth unemployment in Kenya has had little investigation on, which is the reason for this study. The study aims to assess the effect of devolution on youth unemployment rates in Kenya, utilizing Autoregressive Integrated Moving Average-Intervention model. This research was informed by the Keynesian and Decentralization theories of employment, which collectively illustrate how government efforts, like introduction of devolution, are anticipated to influence labor market results. This study used the yearly secondary data on youth unemployment rates from the World Bank covering the period from 1991 to 2022. Computational analysis was done using Python programming. An ARIMA (0, 0, 0)(0,0,1)[4] was selected as the most suitable model for the youth unemployment rates prior to devolution (noise model) due to its lowest Akaike Information Criterion (AIC) value of 234.746 in comparison to other identified candidate models. By including devolution as an intervention in the selected noise model, its statistical significance was established at the 0.05 level of significance. Comparative analysis findings revealed that the average youth unemployment rate increased from 6.67% prior to devolution to 10.19% during the devolution period. The projected counterfactual rate during devolution was approximated to be 8.583%, which confirmed the observed increase as statistically significant. In conclusion, the effect of devolution was found to be statistically significant, implying that youth unemployment rates increased during devolution, as confirmed by the fitted ARIMA - Intervention model. Based on the upward trend in youth unemployment rates, the study recommended that policymakers prioritize other context specific and targeted interventions to address structural barriers in the youth labour markets. These should include expanding access to skills training and vocational education, fostering youth entrepreneurship through financing and mentorship programs, and aligning education curriculum with labour market needs.
This study is the first to integrate recurrence plots, recurrence quantification analysis (RQA) and short-time Fourier Transform (STFT) to predict cryptocurrency market behaviour. Recurrence plots, RQA statistics and STFT spectrograms were calculated from return data and used as input in random forest algorithms as they are optimal tools for identifying non-linear dynamics in market data and analyse their frequency. Our optimised XGBoost algorithm provided a forecasting AUC above 76.7% and accuracy of 70% in predicting increasing or decreasing returns. This highlights the model’s ability to support cryptocurrency investment decision-making within an interpretable machine learning framework.
The Ethereum platform is booming with growing richness and variety in decentralized finance (DeFi) products. However, this progress comes with sophisticated threats, such as sandwich attacks, where attackers exploit the openness and certainty of blockchain technology to manipulate market prices and secure illegal financial rewards through a strategically planned series of transactions. The existing sandwich attack detection methods are ineffective at detecting multi-token transactions and fail to identify multi-token sandwich attacks. To tackle this challenge, this study improves the original detector’s algorithm to identify both traditional single-token and multi-token sandwich attacks. The enhanced system is not only responsive and accurate but also capable of detecting and alerting potential multi-token sandwich attacks. It has been successfully integrated with the go-Ethereum client (Geth). The system is performance-optimized with an average processing time of 0.81 seconds per block and an accuracy rate of 96.17%. The response time for detecting new blocks in real-time is usually no more than 4 seconds, with most between 2 and 3 seconds, which meets practical application requirements. By carefully analyzing the transaction data flow, this system is not only able to identify the traditional front-running attack and sandwich attack, but also extends to multi-currency complex attack strategies. The core innovation lies in the system’s ability to accurately detect and provide early warnings of multi-token sandwich attacks through real-time analysis of in-block transactions, all while maintaining the overall operational efficiency of the node.
Non-fungible token (NFT) markets present a dual analytical challenge: integrating heterogeneous data modalities (high-dimensional visual features and discrete transaction sequences) while preserving privacy for sensitive wallet addresses and trading strategies. Current approaches analyze visual attributes or transaction patterns in isolation, missing critical value drivers from cross-modal interactions. Meanwhile, existing multimodal techniques lack formal privacy guarantees, exposing participants to inference attacks. This article introduces PrivaMod, a privacy-preserving Bayesian framework that addresses these limitations through uncertainty-aware multimodal fusion. Our approach implements precision-weighted Bayesian fusion that dynamically adjusts modality contributions based on quantified uncertainty levels, while integrating Rényi Differential Privacy throughout the pipeline via calibrated noise injection and adaptive gradient clipping. Evaluated on 167,492 CryptoPunk transactions, PrivaMod achieves a market efficiency score of 0.874 and R 2 of 0.912, outperforming existing methods by 13.4% through superior cross-modal integration while maintaining strong privacy guarantees ( \(\varepsilon\) = 0.08, \(\delta\) = 1e-5) with membership inference attack success rates near random guessing (53.4%). The system demonstrates that privacy-preserving techniques can enhance rather than compromise analytical performance, establishing a foundation for responsible market analysis. To ensure reproducibility, we release our code, preprocessed datasets, and model checkpoints with detailed documentation and scripts to replicate all experiments. PrivaMod is available at https://github.com/kvjunior/PrivaMod/blob/main/README.md .
Open access
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Tujuan penulisan artikel ini untuk menyajikan analisis terhadap penerapan blockchain dalam pengembangan smart contract di Indonesia. Penulisan artikel ini didasarkan pada metode penelitian hukum normatif dengan pendekatan yuridis normatif (konsep dan perundang-undangan) dan yuridis kontekstual. Hasil penelitian menunjukan bahwa Validitas penerapan teknologi enkripsi (encrypted technology) antara lain blockchain dalam penggunaan smart contract di Indonesia Telah berkembang tidak lagi terbatas pada transaksi jual beli saja tidak serta-merta menghilangkan peran notaris Validitas smart contract berdasarkan peraturan perundangan yang mengatur tentang kontrak serta keterkaitannya dengan eksistensi notaris di Indonesia bahwa smart contract memiliki validitas hukum di Indonesia selama memenuhi unsur sahnya perjanjian sebagaimana diatur dalam Pasal 1320 KUH Perdata, oleh karena itu, dalam praktik hukum di Indonesia, penggunaan smart contract sebaiknya dikombinasikan dengan akta notaris, terutama untuk transaksi bernilai tinggi atau yang melibatkan kepemilikan dan hak atas benda
This study analyzes the Indonesian Ulema Council's (MUI) fatwa on cryptocurrency and its implementation in Indonesia. Using a qualitative approach with content analysis, we examined official documents, articles, and research reports, and conducted interviews with experts in Islamic economics and information technology. We also collected quantitative data on cryptocurrency users and transactions from reliable sources. Our findings show that MUI has prohibited cryptocurrency due to its uncertainty and potential harm, as per the 2021 fatwa. However, despite this prohibition, many Indonesians continue to use cryptocurrency, with 60% of survey respondents reporting ownership or investment experience. Keywords: Fatwa, Indonesian Ulema Council (MUI), Cryptocurrency, Islamic Perspective, Digital Assets.