Mohamad Sheikho Al Jasem, Trevor De Clark, Ajay Kumar Shrestha
The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era.
Andrea Michienzi, Laura Pollacci, Barbara Guidi, Francesco Maggio
Nowadays, Social Media represents an important window to address societal issues and promote social causes. However, Social Media suffer from several issues concerning fake news, misinformation, disinformation, etc. To address these issues, decentralization has been proposed to overcome current limitations. Blockchain-based Online Social Media (BOSM) offer verifiable platforms, usually enriched with reward systems that allow users to get paid according to the social value they create. Reward systems can economically empower creators and other individuals beyond high-quality content, allowing content creators to earn income. Considering the widespread use of BOSM platforms and various incentive methods, tools are needed to analyze and guide these rewarding strategies to avoid the risk of speculative mechanisms. In this paper, we propose BISON, a predictive and interpretable framework for identifying the drivers of success in blockchain-native articles. BISON can model success not as a purely financial outcome, but as a composite function of content attributes and user engagement patterns, as recorded on the blockchain. Its modular architecture allows for empirical validation across multiple datasets and makes it adaptable to other Web3 platforms. Additionally, our framework introduces Explainable AI into the blockchain content domain.
Decentralized Autonomous Organizations (DAOs) are a class of Decentralized Applications (DApps) using smart contracts to facilitate governance processes. The design of DAOs is affected by additional complexity compared to other DApps due to the need to specify organizational roles, permissions, and control relations early in the early development stages of the system. In addition, DAOs face scalability challenges. While existing Model-Driven Development (MDD) tools support general smart contract and DApp design, they lack constructs tailored to the organizational and governance features unique to DAOs. To bridge this gap, we develop a code generation approach for DAO-ML, a visual modeling language for DAO design. The translator we implement for this method generates smart contracts of DAOs with suitably configured roles and permissions from visual models. The generated smart contracts particularly optimize the representation of roles and permissions to improve the system scalability and handle complex governance structures, necessary to increase the utility of DAO systems. The approach is evaluated in the context of an in vivo case study on the development of a DAO for the disintermediated management of local tourism. This work advances MDD for decentralized systems by bridging high-level governance modeling with executable, gas-efficient smart contract code generation.
Ramesh Adhikari, Costas Busch, Dariusz R. Kowalski
In blockchain sharding, $n$ processing nodes are divided into $s$ shards, and each shard processes transactions in parallel. A key challenge in such a system is to ensure system stability for any ``tractable'' pattern of generated transactions; this is modeled by an adversary generating transactions with a certain rate of at most $ρ$ and burstiness $b$. This model captures worst-case scenarios and even some attacks on transactions' processing, e.g., DoS. A stable system ensures bounded transaction queue sizes and bounded transaction latency. It is known that the absolute upper bound on the maximum injection rate for which any scheduler could guarantee bounded queues and latency of transactions is $\max\left\{ \frac{2}{k+1}, \frac{2}{ \left\lfloor\sqrt{2s}\right\rfloor}\right\}$, where $k$ is the maximum number of shards that each transaction accesses. Here, we first provide a single leader scheduler that guarantees stability under injection rate $ρ\leq \max\left\{ \frac{1}{16k}, \frac{1}{16\lceil \sqrt{s} \rceil}\right\}$. Moreover, we also give a distributed scheduler with multiple leaders that guarantees stability under injection rate $ρ\leq \frac{1}{16c_1 \log D \log s}\max\left\{ \frac{1}{k}, \frac{1}{\lceil \sqrt{s} \rceil} \right\}$, where $c_1$ is some positive constant and $D$ is the diameter of shard graph $G_s$. This bound is within a poly-log factor from the optimal injection rate, and significantly improves the best previous known result for the distributed setting by Adhikari et al., SPAA 2024.
This paper considers five extensions for Chromium-based browsers in order to determine how effective can browser-based defenses against cryptojacking available to regular users be. We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking, CoinEater and Miners Shield, which claim to be designed specifically to identify and stop illegal cryptocurrency mining. An empirically confirmed dataset of 373 distinct cryptojacking-infected websites which was assembled during multi-stage procedure, was used to test those extensions. The results showed that all plugins in question had significant performance limits. Easy Redirect and Miners Shield only blocked 6 and 5 websites respectively, while MinerBlock had the greatest detection rate at only 27% (101/373 sites blocked). Most concerningly, despite promises of cryptojacking prevention, AdGuard (which has over 13 million users) and CoinEater were unable to identify any of the compromised websites. These results demonstrate serious flaws in cryptojacking detection products targeted for regular users, since even the best-performing specimen failed to detect 73% of attacks. The obvious difference between advertised capabilities and real performance highlights the urgent need for either accessibility improvements for laboratory-grade detection technologies that show 90%+ efficiency in controlled environment or fundamental upgrades to current commonly used extensions.
This chapter investigates the potential impact of applying blockchain technology to corporate governance by presenting previous studies, corporate cases, and the European Blockchain Service Infrastructure initiative. The adaptation of blockchain technology to corporate governance can help decentralize the concentration of information by large Information and Communications Technologies global companies such as Google, Apple, Facebook, and Amazon (GAFA), and bring it back to the original internet. This also affects the corporate governance of GAFA. By applying blockchain technology to evaluate corporate governance, the transaction logs conducted by companies in real time can be recorded and preserved on this distributed ledger, enabling all participants to simultaneously share a large amount of information. This transaction history is difficult to tamper with using blockchain technology and enables real-time control and auditing of companies by stakeholders. This chapter proposes the application of blockchain technology to stakeholder management based on the fundamental concept of corporate governance and presents the possibility of solving environmental problems on a global scale and centralized concentration in the corporate world.
Statistical witness indistinguishability is a relaxation of statistical zero-knowledge which guarantees that the transcript of an interactive proof reveals no information about which valid witness the prover used to generate it. In this paper we define and initiate the study of QSWI, the class of problems with quantum statistically witness indistinguishable proofs. Using inherently quantum techniques from Kobayashi (TCC 2008), we prove that any problem with an honest-verifier quantum statistically witness indistinguishable proof has a 3-message public-coin malicious-verifier quantum statistically witness indistinguishable proof. There is no known analogue of this result for classical statistical witness indistinguishability. As a corollary, our result implies SWI is contained in QSWI. Additionally, we extend the work of Bitansky et al. (STOC 2023) to show that quantum batch proofs imply quantum statistically witness indistinguishable proofs with inverse-polynomial witness indistinguishability error.
Timed signatures are cryptographic primitives that enable senders to predefine the validity period of a signature. Currently, two primary types of timed signatures have been developed. The first type, known as Verifiable Timed Signatures (CCS'2020), implements a delay before a signature becomes effective. The second type is Short-Lived Signatures (ASIACRYPT'2022), which allows for the setting of an expiration time for signatures upon creation. However, certain applications requiring time-sensitive authorization demand both activation and expiration times to be set, a requirement not fulfilled by the existing timed signature schemes. To overcome this limitation, we propose a novel flexible timed signature scheme called Time Interval Signatures (TIS). TIS combines Verifiable Delay Functions and Short-Lived Signatures with our Zero-Knowledge Proof of Product, facilitating the flexible setting of both activation and expiration times for the signature. Building on TIS, we present TimeGuardian, a time-bound NFT rights protocol that enables presetting authorization and revocation periods for NFT usage rights. Experimental results show that TIS achieves signature size reductions of 98.67% and 57.14% compared to existing verifiable timed signature solutions.
This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially in the tails of the distribution. Our findings reveal significant upper and lower–upper tail dependencies, suggesting that extreme trading volumes are associated with both positive and negative return extremes. The results confirm a nonlinear and asymmetric volume–return relationship, which traditional linear models fail to capture.
The rapid growth of Ethereum has spurred widespread adoption of smart contracts, enabling substantial financial transactions. Once deployed on the blockchain, smart contracts are immutable, rendering them unmodifiable even if vulnerabilities are present. In recent years, numerous attacks exploiting these vulnerabilities have caused significant financial losses. Although prior research has improved vulnerability detection in source code or bytecode before deployment, identifying attacks that exploit vulnerabilities during the execution phase after deployment remains a significant challenge. These challenges arise from the limited adaptability of predefined detection rules and an overreliance on opcode sequence names, which often neglects a comprehensive analysis of opcode sequence properties. In this study, we propose an advanced multidimensional feature fusion technique designed to detect attacks during the execution phase of smart contracts. By leveraging deep learning, our approach enhances detection accuracy through a comprehensive analysis of attack behaviors across four dimensions: operation objects, action behaviors, functional categories, and gas consumption. Extensive experiments demonstrate that our method achieves a detection accuracy of 97.21% and a weighted F1-score of 97.21%, confirming its effectiveness in identifying attacks.
Designing secure electronic voting systems that truly protect voter privacy, ensure vote accuracy, and allow independent verification continues to pose serious difficulties. Many current cryptographic approaches require excessive computational resources and use encryption keys that are too large for practical implementation. This paper proposes modifications to the Chaum, Pedersen and Cramer, Franklin, Schoenmakers, and Yung voting protocols by integrating elliptic curve cryptography (ECC), which offers stronger security per bit and more compact key representations. The use of ECC allows for reduced parameter sizes while maintaining resistance against known attacks, including those targeting the discrete logarithm problem. We present detailed adaptations of these protocols on elliptic curves and demonstrate how they preserve core security properties such as vote secrecy, universal verifiability, and resistance to double voting under a more efficient cryptographic framework. Our findings contribute to the development of scalable, high-assurance e-voting mechanisms suitable for modern digital infrastructures. The presented modifications significantly enhance the scalability and efficiency of e-voting systems without compromising cryptographic strength.
Blockchain is a decentralized framework that distributes and secures data across a network, eliminating the need for centralized control. Its potential has been especially noted in the field of digital voting, where traditional methods often struggle with issues like tampering, limited transparency, and lack of trust. This research sets up a private Ethereum environment using Geth, configured with a Proof of Authority (PoA) consensus mechanism, to deploy a custom voting smart contract. Developed in Solidity and implemented via the Remix IDE, the contract is designed to enforce secure and efficient voting. The performance of the private PoA network was then compared with the Ethereum Sepolia public test network and private blockchain network Kurtosis using Proof of Stake (PoS). Experimental results show that Geth PoA successfully executed all smart contract functions with significantly lower gas costs and faster block times compared to Sepolia and Kurtosis. These findings demonstrate that Geth PoA offers a lightweight, efficient, and reliable platform for running smart contracts in controlled environments such as institutions or organizations. Based on these results, we can conclude that Geth PoA is a solid option for running smart contracts.
Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin.
Decentralized technologies such as blockchain and federated learning have emerged as promising solutions to improve privacy, transparency, and security in distributed environments. This paper aims to provide updated research directions concerning the unresolved issues of linkability and traceability in decentralized technology transactions. A systematic review was conducted using Scopus and Web of Science databases, covering studies published between 2017 and 2023. A total of 313 papers were initially identified, screened, and filtered based on inclusion and exclusion criteria, resulting in 29 relevant studies. The analysis indicates that most prior works focused on privacy preservation and incentive mechanisms but neglected linkability and traceability concerns. Several approaches, including ring signatures, CryptoNote protocols, and smart contract-based incentives, were identified as potential solutions. While blockchain–federated learning integration enhances privacy, unresolved traceability and linkability issues still pose significant risks in sensitive domains such as healthcare and finance. Future work should prioritize addressing these issues to ensure secure, anonymous, and scalable decentralized transactions.
Anthony Sai Richardo, Franz Adeta, Yohan Muliono, Michelle Hamjaya · 5 authors
Web3 airdrops have become a popular way to distribute tokens and raise project awareness, but their objective is frequently abused by bot activities. For example, the 2024 Hamster Kombat project reported detecting over 2.3 million automated bot interactions during its airdrop event. To address the problem, this research compares seven supervised machine learning models for detecting bot activity in Telegram-based Web3 airdrops by analyzing patterns in API requests. A total of 2600 data entries were collected: 1300 from real bot scripts and 1300 manually gathered using Telegram's built-in network tools. Each sample contains technical features such as HTTP request methods, URLs, request headers, and public IP addresses. These were further enriched with indicators of VPN usage, proxy connections, TOR relay presence, and whether the IP address was linked to a hosting provider. The result shows Gaussian Naïve Bayes and the MLP Classifier were the top performers, with$\mathbf{9 4. 4 1 \%}$validation accuracy,$\mathbf{9 4. 0 0 \%}$test accuracy, and 84.56 % accuracy when evaluated on a separate set of new data. These models accurately captured statistical patterns in bot data and complex interactions in human data. The results emphasize the importance of machine learning in securing Web3 token distribution processes.
This paper presents a novel multi-layered hybrid security approach aimed at enhancing lightweight encryption for IoT-Cloud systems. The primary goal is to overcome limitations inherent in conventional solutions such as TPA, Blockchain, ECDSA and ZSS which often fall short in terms of data protection, computational efficiency and scalability. Our proposed method strategically refines and integrates these technologies to address their shortcomings while maximizing their individual strengths. By doing so we create a more reliable and high-performance framework for secure data exchange across heterogeneous environments. The model leverages the combined potential of emerging technologies, particularly Blockchain, IoT and Cloud computing which when effectively coordinated offer significant advancements in security architecture. The proposed framework consists of three core layers: (1) the H.E.EZ Layer which integrates improved versions of Hyperledger Fabric, Enc-Block and a hybrid ECDSA-ZSS scheme to improve encryption speed, scalability and reduce computational cost; (2) the Credential Management Layer independently verifying data integrity and authenticity; and (3) the Time and Auditing Layer designed to reduce traffic overhead and optimize performance across dynamic workloads. Evaluation results highlight that the proposed solution not only strengthens security but also significantly improves execution time, communication efficiency and system responsiveness, offering a robust path forward for next-generation IoT-Cloud infrastructures.
Internet of Things (IoT) devices constantly generate heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated learning and lifelong learning. However, the extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks. This problem is exacerbated by the single point of failure. Furthermore, the single point of trust created by the central server hinders reliable auditing for long-term threats. Blockchain technology provides a tamper-proof foundation for trustworthy FLL. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on resource-constrained IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning with minimal on-chain disclosure and bidirectional verification. LiFeChain is the first blockchain tailored for FLL. It incorporates two complementary mechanisms: the Proof-of-Model-Correlation (PoMC) consensus on the server, which couples learning and unlearning mechanisms to mitigate negative transfer; and Segmented Zero-knowledge Arbitration (Seg-ZA) at the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is a plug-and-play component that can be seamlessly integrated into existing FLL algorithms for IoT applications. To demonstrate its practicality and performance, we implement LiFeChain in representative FLL algorithms with Hyperledger Fabric under 6 attacks. Theoretical analysis and extensive evaluations demonstrate that LiFeChain effectively mitigates long-term attacks, and significantly reduces latency and storage overhead compared to state-of-the-art blockchain solutions.
This paper presents a machine learning framework for the early detection of rug pull scams on decentralized exchanges (DEXs) within The Open Network (TON) blockchain. TON's unique architecture, characterized by asynchronous execution and a massive web2 user base from Telegram, presents a novel and critical environment for fraud analysis. We conduct a comprehensive study on the two largest TON DEXs, Ston.Fi and DeDust, fusing data from both platforms to train our models. A key contribution is the implementation and comparative analysis of two distinct rug pull definitions--TVL-based (a catastrophic liquidity withdrawal) and idle-based (a sudden cessation of all trading activity)--within a single, unified study. We demonstrate that Gradient Boosting models can effectively identify rug pulls within the first five minutes of trading, with the TVL-based method achieving superior AUC (up to 0.891) while the idle-based method excels at recall. Our analysis reveals that while feature sets are consistent across exchanges, their underlying distributions differ significantly, challenging straightforward data fusion and highlighting the need for robust, platform-aware models. This work provides a crucial early-warning mechanism for investors and enhances the security infrastructure of the rapidly growing TON DeFi ecosystem.
Quantum computing stands poised to transform numerous fields of modern technology by offering computational capabilities beyond those of classical systems. This survey offers a detailed analysis of major fields, such as artificial intelligence and machine learning (AI/ML), blockchain, cybersecurity, and digital communication, highlighting how they are significantly transformed through advancements in quantum computing. It presents a comparative analysis of current quantum computing paradigms and architectures, and examines major quantum algorithms such as Shor’s integer factorization algorithm, Grover’s search algorithm, and hybrid quantum–classical approaches like QAOA and VQE, highlighting their implications for real-world problem solving. Significant advancements in quantum hardware are surveyed, from increasing qubit counts and improved coherence to progress in error mitigation and emerging quantum processor technologies, and their impact on near-term and long-term computing capabilities is evaluated. Finally, the current limitations of quantum computing are discussed, and forward-looking insights into future research directions are provided, outlining the path toward fully harnessing quantum power across industries.