Bruno E. Holtz, Ricardo S. Ehlers, Adriano K. Suzuki, Francisco Louzada
Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the SMSN family (DynSSV-SMSN), using priors that penalize model complexity. Parameter estimation was carried out using the Hamiltonian Monte Carlo (HMC) method via the \texttt{RStan} package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO-CV information criteria.
Ye Li, Mengliang Li, Dehai Zhao, Jiamou Sun · 5 authors
Solidity, the dominant smart contract language for Ethereum, has rapidly evolved with frequent version updates to enhance security, functionality, and developer experience. However, these continual changes introduce significant challenges, particularly in compilation errors, code migration, and maintenance. Therefore, we conduct an empirical study to investigate the challenges in the Solidity version evolution and reveal that 81.68 % of examined contracts encounter errors when compiled across different versions, with 86.92 % of compilation errors. To mitigate these challenges, we conducted a systematic evaluation of large language models (LLMs) for resolving Solidity compilation errors during version migrations. Our empirical analysis across both open-source (LLaMA3, DeepSeek) and closedsource (GPT-4o, GPT-3.5-turbo) LLMs reveals that although these models exhibit error repair capabilities, their effectiveness diminishes significantly for semantic-level issues and shows strong dependency on prompt engineering strategies. This underscores the critical need for domain-specific adaptation in developing reliable LLM-based repair systems for smart contracts. Building upon these insights, we introduce SMCFIXER, a novel framework that systematically integrates expert knowledge retrieval with LLM-based repair mechanisms for Solidity compilation error resolution. The architecture comprises three core phases: (1) context-aware code slicing that extracts relevant error information; (2) expert knowledge retrieval from official documentation; and (3) iterative patch generation for Solidity migration. Experimental validation across Solidity version migrations demonstrates our approach's statistically significant 24.24% improvement over baseline GPT-4o on real-world datasets, achieving near-perfect 96.97% accuracy.
Bernhard Kauer, Aleksandr Petrosyan, Benjamin Livshits
The fundamental basis for maintaining integrity within contemporary blockchain systems is provided by authenticated databases. Our analysis indicates that a significant portion of the approaches applied in this domain fail to sufficiently meet the stringent requirements of systems processing transactions at rates of multi-million TPS. AlDBaran signifies a substantial advancement in authenticated databases. By eliminating disk I/O operations from the critical path, implementing prefetching strategies, and refining the update mechanism of the Merkle tree, we have engineered an authenticated data structure capable of handling state updates efficiently at a network throughput of 50 Gbps. This throughput capacity significantly surpasses any empirically documented blockchain throughput, guaranteeing the ability of even the most high-throughput blockchains to generate state commitments effectively. AlDBaran provides support for historical state proofs, which facilitates a wide array of novel applications. For instance, the deployment of AlDBaran could enable blockchains that do not currently support state commitments to offer functionalities for light clients and/or implement rollups. When benchmarked against alternative authenticated data structure projects, AlDBaran exhibits superior performance and simplicity. In particular, AlDBaran achieves speeds of approximately 48 million updates per second using an identical machine configuration. This characteristic renders AlDBaran an attractive solution for resource-limited environments, as its historical data capabilities can be modularly isolated (and deactivated), which further enhances performance. On consumer-level portable hardware, it achieves approximately 8 million updates/s in an in-memory setting and 5 million updates/s with snapshots at sub-second intervals, illustrating compelling and cost-effective scalability.
Amjad Almaghthawi, Wael M.S. Yafooz, Nasser S. Albalawi
In decentralized apps, smart contracts are used to conduct trusted transactions on the Blockchain (BC). While smart contracts are highly effective, they are also highly susceptible to security flaws, leading to serious financial consequences. However, the combination of BC technology and artificial intelligence provides a solution for powerful, secure, and decentralized applications in various sectors. Furthermore, large language models (LLMs), which are essential advanced machine learning frameworks, are now used in various applications, including customer service, chatbots, code generation, vulnerability detection, and language translation. This study investigates the use of LLMs for automated vulnerability detection in Solidity-based smart contracts. Specifically, three models are evaluated and compared: GPT-3.5-turbo, DeepSeek R1, and LLaMA-3. With a labeled, multi-class dataset including four vulnerability types, the models are assessed across three reasoning strategies: zero-shot, few-shot, and chain of thought. A prompt-based evaluation and performance comparison is conducted using standard metrics such as accuracy, precision, recall, F1-score, and average detection time. Results show that in the zero-shot setting, GPT-3.5-turbo achieves the highest accuracy of 94.59%, followed closely by LLaMA-3 with 92%, while DeepSeek R1 achieved 78.95%. In the few-shot setting, LLaMA-3 outperformed other models. Furthermore, in the CoT setting, LLaMA-3 demonstrates the strongest overall performance with 96% accuracy and an F1-score of 0.82, surpassing DeepSeek R1's average of 78.95% and GPT-3.5's CoT performance, which is notably lower. Hence, this study develops an evaluation framework for LLM-based vulnerability detection, and we have demonstrated that prompt engineering has the potential to enhance the security of smart contracts.
Widespread reuse of open-source code in smart contract development boosts programming efficiency but significantly amplifies bug propagation across contracts, while dedicated methods for detecting similar smart contract functions remain very limited. Conventional abstract-syntax-tree (AST) based methods for smart contract similarity detection face challenges in handling intricate tree structures, which impedes detailed semantic comparison of code. Recent deep-learning based approaches tend to overlook code syntax and detection interpretability, resulting in suboptimal performance. To fill this research gap, we introduce SmartDetector, a novel approach for computing similarity between smart contract functions, explainable at the fine-grained statement level. Technically, SmartDetector decomposes the AST of a smart contract function into a series of smaller statement trees, each reflecting a structural element of the source code. Then, SmartDetector uses a classifier to compute the similarity score of two functions by comparing each pair of their statement trees. To address the infinite hyperparameter space of the classifier, we mathematically derive a cosine-wise diffusion process to efficiently search optimal hyperparameters. Extensive experiments conducted on three large real-world datasets demonstrate that SmartDetector outperforms current state-of-the-art methods by an average improvement of 14.01% in F1-score, achieving an overall average F1-score of 95.88%.
Philip Kwaku Adjei, Qin Zhi-guang, Isaac Amankona Obiri, Ansu Badjie · 8 authors
Smart contracts have revolutionized decentralized applications by automating agreement enforcement on blockchain platforms. However, detecting vulnerabilities in smart contract interactions remains challenging due to complex state interdependencies. This paper presents a novel approach using multi-agent Reinforcement Learning (MARL) to identify smart contract vulnerabilities. We integrate a Hierarchical Graph Attention Network (HGAT) into a Multi-Agent Actor-Critic framework, decomposing vulnerability detection into complementary policies: a high-level policy encoding historical interactions and a low-level policy capturing structured actions within contract state spaces. By modeling interactions as multistep reasoning paths, our MARL framework effectively navigates complex transaction sequences and resolves semantic ambiguities across different contract states. Experimental evaluations on real-world blockchain datasets demonstrate significant improvements in detecting multiple vulnerability types. For reentrancy attacks, our model achieves 93.8% accuracy and an 89.8% F1 score. The framework also performs strongly in detecting front running (88.9% accuracy), denial-of-service attacks (91.2% accuracy), and unchecked low-level vulnerabilities (91.6% accuracy), outperforming existing approaches across all vulnerability categories.
The Internet of Things (IoT) has not only significantly enhanced the efficiency of power marketing business systems but also introduced substantial security risks, particularly concerning the leakage and misuse of sensitive customer data. The current existence of a variety of data security auditing programs is more or less flawed, unable to comprehensively rule out the risk of data leakage. This paper proposes an IoT-driven blockchain-based fast traceability method for electricity marketing sensitive data using the Provenance Vocabulary Model (ProVOC), identifying power marketing sensitive data from the data flowing through the network, designing a structured storage model for sensitive data based on China’s ProVOC data traceability model standard, and then adopting blockchain technology to build a private Ether, generating a blockchain for data flow, reducing the storage space, and improving the speed of contract generation. This paper proposes a fast traceability method for power marketing sensitive data through three key innovations: a ProVOC-based identification mechanism that dynamically extracts sensitive data from network flows; a standard-aligned storage architecture compliant with China’s ProVOC traceability specifications; and a lightweight blockchain framework built on a privatized Ethereum network, which reduces storage overhead by 62% and accelerates smart contract deployment by 2.3 × compared to conventional approaches.
Sandeep Gajanan Sutar, B M Praveen, Amolkumar N. Jadhav
Cloud computing has transformed data storage and access with flexible and scalable solutions. However, its dependence on third-party services poses significant concerns regarding data privacy and integrity. To tackle these concerns, blockchain technology known for its decentralized structure and tamper resistance offers a promising security enhancement for cloud environments. This paper explores and evaluates various blockchain-based mechanisms for securing cloud data and proposes a hybrid model that integrates blockchain with existing cloud infrastructures. Leveraging consensus protocols and cryptographic hashing, the proposed approach aims to mitigate data breaches, unauthorized access, and tampering. A practical implementation demonstrates the model’s effectiveness in fostering trust, transparency, and reliability in cloud services. Keywords: Block-chain, Data Privacy, Security, Data Integrity, Distributed ledger, Cloud computing
The manufacturing and distribution of counterfeit tablets, especially in developing countries, is an urgent and increasingly critical global problem. Falsified medicinal products may contain incorrect ingredients and doses. One of the reasons for drug counterfeiting is the imperfect supply chain system in the pharmaceutical industry. Medicinal products are moved between manufacturers, suppliers, wholesalers, retailers, and pharmaceutical firms before meeting consumers. This study proposes PharmChain, a scenario-oriented drug traceability and regulation blockchain framework that reconstructs the entire service infrastructure by splitting the service provider into three separate service components and ensuring the authenticity and privacy of traceability details. PharmChain can track medication development via patient supply in the pharmaceutical industry. An Ethereum-based blockchain stores the transactions, and only trusted parties can access the data through the chain. We create and test our smart contract code in the Remix environment. We present detailed cost and security analyses incurred by supply chain stakeholders. We also use cost analysis to assess the performance of the proposed solution and demonstrate its affordability. • Introduce PharmChain, a data-driven scenario-based drug traceability and regulation blockchain framework. • Ensure authenticity and privacy of traceability data, enhancing security. • Validate smart contract code rigorously in the Remix environment. • Show the feasibility and affordability of PharmChain through detailed analytics. • Alleviate counterfeit tablet distribution, which poses a pressing and escalating global challenge.
Financial decisions in production systems must satisfy a layered set of obligations: risk tolerance, regulatory compliance, fairness constraints, privacy requirements, and operational service levels.Most machine learning models optimize predictive objectives but treat policy and compliance as external checks.This separation creates avoidable failure modes: decisions that are accurate yet non-compliant, long audit cycles, and limited customer recourse.This paper proposes Policy-Carrying Decision Models (PCDMs): decision systems that emit not only an outcome (approve/decline/route) and calibrated confidence, but also a machine-checkable proof that the decision adhered to an explicit policy expressed in a domain-specific language (FinPol).At inference time, the model (and its surrounding decision logic) produces a decision receipt containing the outcome, explanations scoped to permissible disclosure, and a verifiable policy proof.Optionally, a zero-knowledge variant allows third parties to verify compliance without access to sensitive features or thresholds.
Ruba Islayem, Senay A. Gebreab, Walaa AlKhader, Ahmad Musamih · 7 authors
Traditional health insurance claim processing systems are plagued by inefficiencies and vulnerabilities, often resulting in significant financial losses due to fraudulent activities. Existing fraud detection methods are largely manual, time-consuming, and inadequate for handling the complexity and scale of modern fraudulent schemes. Moreover, the trust-based relationships between insurers and healthcare providers lack mechanisms to ensure data integrity and prevent manipulation. While several blockchain-based systems have been proposed to improve transparency and tamper resistance, they typically focus on structured data and predefined fraud types, offering limited adaptability and analytical insight. This paper proposes a novel solution leveraging blockchain technology and Large Language Models (LLMs) to transform fraud detection. The system uses Ethereum smart contracts (SCs) to securely store medical records and claim details on a decentralized, tamper-proof ledger that ensures data integrity, traceability, and accountability. This immutable data is accessed by an LLM via a Retrieval-Augmented Generation (RAG) system, which enables intelligent retrieval and analysis of relevant clinical information to detect fraud patterns and inconsistencies. To support complex scenarios involving free-text documents, unstructured clinical data, such as lab reports, are stored using decentralized off-chain storage and retrieved during LLM analysis. In addition, an LLM-powered chatbot also allows insurance providers to interact with the system in natural language for claim inquiries, explanations, and summaries. The architecture, sequence diagrams, and implementation algorithms outline the development process, while testing scenarios demonstrate the system's ability to detect fraud such as inflated costs, unnecessary treatments, and unrendered services. Evaluation using both synthetic and public clinical datasets showed strong performance, with the LLM achieving up to 99% fraud detection accuracy. Cost, security, and scalability analyses confirm the system's practicality and resilience, with the complete detection process executing in just 13 seconds. By overcoming the limitations of traditional systems, this framework offers a scalable and adaptable approach for healthcare and other domains. The SCs and source code are publicly available on GitHub.
The Internet of Things (IoT) has attained significant interest recently, particularly in the medical field due to the quick development of IoT devices. Medical related data contains a significant volume of personal information, and it is crucial to maintain privacy. As medical information becomes increasingly electronic in the era of big data, securely and accurately storing medical information is critical. However, the heterogeneity of information systems poses a significant challenge to their sharing. Moreover, medical data typically comprises sensitive information, and sharing it can potentially lead to breaches of personal privacy. Data sharing is a significant concern in healthcare because of privacy leakage and security issues. To combat this issue, this paper introduces the prediction and Provable Partitioned Secure Block Chain Principle (PPSBCP) technique is used to secure healthcare data sharing. Initially, in the healthcare data analysis phase, the Preprocessing and normalization are carried out by Z-score normalized for analysing the healthcare-sensitive margins. The SSIR (Sensitive Spectral Impact Rate) method is applied to find the sensitive records. Based on the impact margins, the Binomial Quadratic Sensitive Data Prediction (BQSDP) method is applied to categorize the sensitive and non-sensitive information. In the blockchain phase, create a Hash Index Policy (HIP) to encrypt the data using a Foldable Blockchain Encryption Standard (FBES). The Master Node Handover Authentication Policy (MNHAP) is applied to verify the private key in the data safety. The Distributed Hyper Ledger Mechanism (DHLM) is applied to make the chain transaction principle. The proposed system accomplishes high performance in security by achieving the parameters in verification and validation as well as compared to the existing systems.
The legal status of mining in Russia remains one of the most controversial issues. The main difficulty is related to the lack of a clear approach to the legal regulation of this process, which is the creation of new units of cryptocurrency. Nevertheless, the regulatory legal acts adopted last year emphasize the relevance and importance of the analyzed topic. The article examines the problems of qualifying crimes related to obtaining cryptocurrency, including mining. The legal status of cryptocurrencies in Russian and international legislation is analyzed, identifying gaps in regulation and enforcement. Special attention is given to the qualification of illegal mining as a form of unlawful business, as well as crimes related to electricity theft, fraud, extortion, and money laundering. The study explores relevant court rulings and evidentiary issues in criminal cases involving digital assets. International experiences in cryptocurrency regulation are reviewed, and suggestions for improving Russian legislation are provided. Key directions for the development of criminal law policy concerning cryptocurrency-related crimes are highlighted.
The robustness and reliability of blockchain applications, critically depend on thorough testing. This study introduces AGTS, an automated framework for generating smart contract test suites on Hyperledger Fabric, significantly reducing manual effort while improving test coverage and security. The framework integrates static and dynamic analysis, leveraging symbolic execution and fuzz testing to detect vulnerabilities. The project is implemented in C#, and uses three test cases: CoCoME, LibraryMS and LoanPS, with their requirements as input, and generates their closely related test cases. We generated 34 test cases for CoCoME, 32 for LibraryMS, and 28 for LoanPS. Combining the test cases, their executable scripts were generated. Finally, we tested all the targeted smart contracts rigorously against the predefined criteria. The generated scripts undergo the quality assurance using ShellCheck, deploying on the continuous integration system, that further enhances the reliability and maintainability of these tests. AGTS automates the entire process of test script creation and verification, drastically reducing the manual effort involved in preparing test cases. It facilitates rapid and thorough testing cycles by generating dedicated scripts that execute specific test scenarios on smart contracts, verifying their behavior and performance. By automating the tedious and error-prone task of writing test cases, AGTS accelerates the development process and fortifies the security framework of blockchain applications. Comprehensive evaluation of AGTS across diverse real world smart contracts demonstrated the effectiveness in defect detection, significantly outperforming existing methods. The contribution of AGTS extends beyond providing a practical testing tool, by offering developers a robust framework for ensuring the reliability and security of their smart contracts. AGTS not only expedites test preparation but also ensures comprehensive test coverage, thus improving the overall quality of blockchain applications.
ABSTRACT This study aims at bridging critical gaps in the existing cryptocurrency research by exploring combinations of technological, macroeconomic and behavioural factors, namely, economic agents' expectations and the size of influence that each of them has on the Bitcoin price movements. In contrast to the existing studies that focused on individual determinants and estimated aggregate effects thereof, in this study, fuzzy‐set qualitative comparative analysis (fsQCA) is applied to determine configurations of drivers to determine the Bitcoin price and used necessary condition analysis (NCA) to quantify the magnitude of the effects using the monthly data between 2011 and 2022. Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.
Abdullah Ayub Khan, Asif Ali Laghari, Hamad Al-Mansour, Leila Jamel · 8 authors
The multimedia environment has undergone significant growth, particularly in the area of multimedia data and its migration to cloud platforms, which has raised issues about security, confidentiality, data integrity, and privacy protection. While Blockchain Distributed Ledger Technology (BDLT) offers decentralized trust and transparency the advent of Quantum Computing threatens classical cryptographic primitives, which make multimedia data increasingly vulnerable. This paper proposes a novel and secure framework that collaborates BDLT with quantum-resilient, mainly known post-quantum cryptographic schemes to ensure long-term data integrity and privacy preservation in cloud-based infrastructures. Due to this, the proposed solution enables secure, efficient, and transparent that helps in public auditing of multimedia content without compromising stakeholder confidentiality. It leverages Zero-Knowledge Proofs (ZKPs), lattice-based cryptography, and smart contract automation, which model fortifies data authenticity verification against quantum attacks. Simulation results illustrate the effectiveness of the proposed framework that achieves a 98.21% accuracy in data integrity verification, a 96.84% reduction in quantum vulnerability, and an 87.85% efficiency gain in auditing speed compared to classical BDLT-enabled platforms. In addition, privacy leakage in multimedia systems is reduced by 92.47% proving the framework’s robustness. This solution underscores the potential of synergizing BDLT, quantum secure cryptography, and cloud computing to build a future-proof solution for privacy-protected multimedia data management and public auditing.
Open access
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques
Blockchain technology has emerged as one of the most revolutionary innovations in the digital age, offering decentralized, transparent, and immutable solutions to a wide range of industries.However, the growing adoption of blockchain systems, from cryptocurrencies to enterprise applications, has brought to the forefront concerns surrounding security.While blockchain is inherently designed to be secure, it is not immune to vulnerabilities, and several risks persist that could undermine its effectiveness.This paper delves into the fundamental security aspects of blockchain technology, beginning with its core principles of decentralization, immutability, and cryptographic integrity.These features are key to the system's resilience, but their effectiveness in the face of sophisticated attacks is contingent upon the proper implementation of security protocols.We explore the most common security challenges facing blockchain networks today, including 51% attacks, smart contract vulnerabilities, Sybil attacks, and doublespending.Each of these issues poses significant risks to blockchain's trustworthiness, scalability, and operational efficiency.Notably, smart contracts, while heralded for their autonomy and trustless execution, have also shown to be prone to coding errors and exploits, which have led to major financial losses.In addition, vulnerabilities tied to cryptographic key management and the consensus algorithms that underpin blockchain, such as Proof of Work (PoW) and Proof of Stake (PoS), are examined in detail.
Renan Yamaguti, Luiz Carlos Branquinho Caixeta Ferreira, Lucas L. Motta, Raphael M. Assumpção · 7 authors
This paper investigates the integration of Internet of things (IoT) technology with blockchain to enhance transparency, accountability, and operational efficiency in smart contract execution for IoT ecosystems. The proposed approach extends the Three-Phase Methodology (TpM) by introducing an innovative entity, the IoT Operator, which acts as a custody caretaker, contract enforcer, and mediator. By leveraging blockchain's secure and immutable ledger, the IoT Operator ensures the reliable monitoring and governance of IoT applications. A PoC implementation conducted at the Eldorado Research Institute demonstrates the methodology's effectiveness, realizing a significant reduction of 95.83% in equipment search time. This work highlights the practical advantages of integrating blockchain and IoT within a structured framework, emphasizing the need for tailored, application-specific solutions rather than generic decentralization. The findings offer actionable guidelines for implementing blockchain in IoT systems, paving the way for more secure, efficient, and resilient IoT applications.
Ruslan Kysil, István András Seres, Péter Kutas, Nándor Kelecsényi
This work explores the application and efficient deployment of (standardized) post-quantum (PQ) digital signature algorithms in the blockchain environment. Specifically, we implement and evaluate four PQ signatures in the Ethereum Virtual Machine: W-OTS+ , XMSS, SPHINCS+, and MAYO. We focus on optimizing the gas costs of the verification algorithms as that is the signature schemes’ only algorithm executed on-chain, thus incurring financial costs (transaction fees) for the users. Hence, the verification algorithm is the signature schemes’ main bottleneck for decentralized applications. We examine two methods to verify post-quantum digital signatures on-chain. Our practical performance evaluation shows that full on-chain verification is often prohibitively costly. Naysayer proofs (FC’24) allow a novel optimistic verification mode. We observe that the Naysayer verification mode is generally the cheapest, at the cost of additional trust assumptions. We release our implementation called poqeth as an open-source library.
Aydin Abadi, Basil Aldali, Benito Vincent, Elliot A. J. Hurley · 13 authors
Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.
In this paper, we present NetMoniAI, an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination. The framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection. A central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness. We evaluated NetMoniAI on a local micro-testbed and through NS-3 simulations. Results confirm that the two-tier agentic-AI design scales under resource constraints, reduces redundancy, and improves response time without compromising accuracy. To facilitate broader adoption and reproducibility, the complete framework is available as open source. This enables researchers and practitioners to replicate, validate, and extend it across diverse network environments and threat scenarios. Github link: https://github.com/pzambare3/NetMoniAI
Federico Calandra, Marco Bernardo, Andrea Esposito, Francesco Fabris
Blockchains are widely recognized for their immutability, which provides robust guarantees of data integrity and transparency. However, this same feature poses significant challenges in real-world situations that require regulatory compliance, correction of erroneous data, or removal of sensitive information. Redactable blockchains address the limitations of traditional ones by enabling controlled, auditable modifications to blockchain data, primarily through cryptographic mechanisms such as chameleon hash functions and alternative redaction schemes. This report examines the motivations for introducing redactability, surveys the cryptographic primitives that enable secure edits, and analyzes competing approaches and their shortcomings. Special attention is paid to the practical deployment of redactable blockchains in private settings, with discussions of use cases in healthcare, finance, Internet of drones, and federated learning. Finally, the report outlines further challenges, also in connection with reversible computing, and the future potential of redactable blockchains in building law-compliant, trustworthy, and scalable digital infrastructures.