Mohammad Madine, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob
In recent years, the healthcare sector has been increasingly challenged in securing patient identities and medical records on blockchain due to rising privacy demands and strict regulatory requirements. Although advanced techniques like self-sovereign identity and zero-knowledge proofs (ZKPs) show promise, these solutions fail to limit unwarranted patient data disclosure effectively. In this paper, we propose a ZKP-based solution that combines STARKs and anonymous credentials to enable anonymous authentication and enhance privacy across both public and private blockchains. Leveraging transparent ZKP schemes and anonymous credentials, our approach ensures unlinkability by preventing the correlation of multiple patient interactions. We present sequence diagrams of real-world interactions, detailed algorithms for on- and off-chain computations, and implement the system on Ethereum and Starknet blockchains. We present a rigorous evaluation of the proposed solution, encompassing smart contract testing on Starknet networks, transaction cost analysis, performance benchmarking, scalability assessment, and static security auditing. The results demonstrate consistent and economically viable transaction costs, millisecond-level execution times for credential issuance, presentation generation, and verification, linear scalability with increasing claim count and size. We compare our solution with state-of-the-art ZKP-based identity systems to demonstrate its superiority. We further discuss its broader applicability beyond healthcare, including domains such as finance, education, and supply chain management. We make the smart contract codes publicly available on GitHub.
Jyoti Neeli, Vijaya Shetty S, N. N. Srivastava, K. Venkatesh Sharma · 5 authors
The Solana blockchain serves as the foundation for the Finance (DeFi) Lending Protocol. The proposed protocol seeks to address the high fees and sluggish transaction speeds of current Ethereum-based protocols by utilizing Solana's high throughput and low-latency infrastructure. Among our contributions are a real-time interest rate model, improved liquidation logic, and a novel lending pool mechanism. According to experimental results, fees are lowered by more than 90% and transaction speeds are increased by 85% when compared to Ethereum- based systems. According to the results, DeFi protocols based on Solana are ideally suited for extensive financial applications.
The main objective of the report is to demonstrate the creation and vulnerability testing of a web3 application for a decentralized system that integrates three key processes – membership management, automatic revenue distribution, and participation in the governance of the organization – within a secure, transparent, and immutable blockchain infrastructure. An analysis is demonstrated to detect and eliminate potential vulnerabilities and integration tests to confirm the correct interaction between the system modules.
Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti money laundering and countering the financing of terrorism (AML CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and the evolution of financial oversight (Kurum, 2023). This paper aims to provide a narrative review of recent RegTech applications in financial crime prevention, with a focus on key compliance domains. A structured literature review was conducted to examine publications between 2020 and 2024 with a thematic synthesis of findings related to customer due diligence (CDD) and know your customer (KYC), transaction monitoring, regulatory reporting and compliance automation, information sharing and cross border cooperation, as well as cost efficiency. Findings reveal that RegTech solutions give financial institutions more responsibility for detecting and managing financial crime risks, making them more active players in compliance processes traditionally overseen by regulators. The combined use of technologies such as artificial intelligence (AI), blockchain, and big data also generates synergistic effects that improve compliance outcomes beyond what these technologies achieve individually. This demonstrates the strategic relevance of integrated RegTech approaches.
Modern high-throughput BFT consensus protocols use streamlined push-pull mechanisms to disseminate blocks and keep happy-path performance optimal. Yet state-of-the-art designs lack a principled and efficient way to exchange blocks, which leaves them open to targeted attacks and performance collapse under network asynchrony. This work introduces the concept of a block synchronizer, a simple abstraction that drives incremental block retrieval and enforces resource-aware exchange. Its interface and role fit cleanly inside a modern BFT consensus stack. We also uncover a new attack, where an adversary steers honest validators into redundant, uncoordinated pulls that exhaust bandwidth and stall progress. Beluga is a modular and scarcity-aware instantiation of the block synchronizer. It achieves optimal common-case latency while bounding the cost of recovery under faults and adversarial behavior. We integrate Beluga into Mysticeti, the consensus core of the Sui blockchain, and show on a geo-distributed AWS deployment that Beluga sustains optimal performance in the optimistic path and, under attack, delivers up to 3x higher throughput and 25x lower latency than prior designs. The Sui blockchain adopted Beluga in production.
Ethan Hicks, Joseph Oglio, Mikhail Nesterenko, Gokarna Sharma
We study financial transaction confirmation finality in Bitcoin as a function of transaction amount and user risk tolerance. A transaction is recorded in a block on a blockchain. However, a transaction may be revoked due to a fork in the blockchain, the odds of which decrease over time but never reach zero. Therefore, a transaction is considered confirmed if its block is sufficiently deep in the blockchain. This depth is usually set empirically at some fixed number such as six blocks. We analyze forks under varying network delays in simulation and actual Bitcoin data. Based on this analysis, we establish a relationship between block depth and the probability of confirmation revocation due to a fork. We use prospect theory to relate transaction confirmation probability to transaction amount and user risk tolerance.
Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.
Preston Vander Vos, Alberto Sonnino, Giorgos Tsimos, Philipp Jovanovic · 5 authors
Blockchain consensus faces a trilemma of security, latency, and decentralization. High-throughput systems often require a reduction in decentralization or robustness against strong adversaries, while highly decentralized and secure systems tend to have lower performance. We present BlueBottle, a two-layer consensus architecture. The core layer, BB-Core, is an n=5f+1 protocol that trades some fault tolerance for a much lower finality latency with a medium-sized core validator set. Our experiments show that BB-Core reduces latency by 20-25% in comparison to Mysticeti. The guard layer, BB-Guard, provides decentralized timestamping, proactive misbehavior detection in BB-Core, and a synchronous recovery path. When it observes equivocations or liveness failures in the core -- while tolerating up to f<3n/5 faulty nodes in the primary layer -- guard validators disseminate evidence, agree on misbehaving parties for exclusion or slashing, and either restart the core protocol (for liveness violations) or select a canonical fork (for safety violations). Together, these layers enable optimistic sub-second finality at high throughput while maintaining strong safety and liveness under a mild synchrony assumption.
Chuanlei Li, Zhicheng Sun, Jing Xin Yuu, Xuechao Wang
Cross-chain interoperability is a core component of modern blockchain infrastructure, enabling seamless asset transfers and composable applications across multiple blockchain ecosystems. However, the transparency of cross-chain messages can inadvertently expose sensitive transaction information, creating opportunities for adversaries to exploit value through manipulation or front-running strategies. In this work, we investigate cross-chain sandwich attacks targeting liquidity pool-based cross-chain bridge protocols. We uncover a critical vulnerability where attackers can exploit events emitted on the source chain to learn transaction details on the destination chain before they appear in the destination chain mempool. This information advantage allows attackers to strategically place front-running and back-running transactions, ensuring that their front-running transactions always precede those of existing MEV bots monitoring the mempool of the destination chain. Moreover, current sandwich-attack defenses are ineffective against this new cross-chain variant. To quantify this threat, we conduct an empirical study using two months (August 10 to October 10, 2025) of cross-chain transaction data from the Symbiosis protocol and a tailored heuristic detection model. Our analysis identifies attacks that collectively garnered over \(5.27\) million USD in profit, equivalent to 1.28\% of the total bridged volume.
Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin's past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes.
Lourdusamy Selvam, S. Abarna, R. Santhana Krishnan, Azarudeen K · 6 authors
Ensuring vaccine integrity requires rigorous cold-chain management, as even minor temperature deviations can compromise safety, cause financial losses, and endanger public health. Conventional monitoring systems, dependent on centralized databases and manual oversight, remain prone to delays, inaccuracies, and tampering. To address these limitations, we propose an integrated framework that combines deep learning and blockchain for secure, predictive, and real-time cold-chain monitoring. A distributed network of IoT sensors captures temperature, humidity, vibration, and GPS data at one-minute intervals. Long Short-Term Memory (LSTM) networks forecast short-term temperature trends, Autoencoders (AE) detect anomalies, and one-dimensional Convolutional Neural Networks (1D CNN) classify shipment states as Safe, At Risk, or Spoiled. A decision engine fuses outputs from these models into a unified risk score, enabling timely and data-driven interventions. Critical events and metadata are immutably recorded on the Ethereum blockchain, while raw sensor data is stored off-chain using IPFS to ensure auditability and efficiency. The system also supports automated alerts, real-time monitoring through Grafana dashboards, and adaptive model retraining for continuous improvement. Evaluation covers forecasting accuracy, anomaly detection precision, classification performance, and blockchain efficiency. By uniting predictive analytics with tamper-proof logging, the proposed framework reduces vaccine spoilage, strengthens supply chain resilience, and offers a scalable solution for broader temperature-sensitive logistics applications.
Abstract In order to design an ideal financial infrastructure, we must preserve the benefits of modern monetary system while effectively utilizing innovative technologies. The current system, with its two-tiered structure of a central bank and commercial banks, supports market-based financial intermediation and elastic supply of money. This is underpinned by the fractional reserve system, banks’ credit creation, banking regulation and deposit insurance. Given that retail Central Bank Digital Currencies (CBDCs) and stablecoins could impact the functions of this modern monetary system, discussions around them often overlap with the “narrow banking” debates of the 20th century. Based on these considerations, tokenized deposits have been developed to maintain the advantages of the two-tiered monetary system while integrating blockchain and distributed ledger technology. Since both digital currencies and digital assets are forms of “digital tokens” that adopt common technologies, it is crucial to establish seamlessly-connected platforms for comprehensively handling transactions of both digital currencies and digital assets.
C.M. Nalayini, A.R. Sathyabama, S. Priyadharshini, Sumathi. S
In the modern agricultural landscape, collaborative robots have emerged as key technology for enhancing precision, efficiency and sustainability in farming practices. This research introduces a Smart Cobot Greenhouse Assistant developed to automate plant health monitoring and management with controlled environment. It identifies plant's condition and take suitable actions like watering dry plants, raising alerts for diseased ones, and leaving healthy plants unharmed. A two-dimensional workspace is modelled where the cobot is initialized at the origin$(\mathbf{0, 0})$and plants are located at respective positions$(\mathbf{x, y})$. Dynamic states such as healthy, dry and diseased are assigned and simulated through python code using a well-defined synthetic dataset. Cobot's movements and actions are animated using Matplotlib visualization to produce an efficient and sustainable greenhouse management and the information are recorded into the blockchain distributed ledger and alerts are sent to the owner for further decision. Compared with the traditional system, the proposed smart cobot achieved operational responsiveness, dynamic visual feedback, and trustworthy data handling with full transparency.
As the application fields of permissioned blockchains broaden and the integration of related industries accelerates, there is a rising demand for permissioned blockchains to support scalable networks. This paper proposes a Partitioned, Parallel and Practicable permissioned blockchain, called as P3-Chain, which builds upon a multi-shard two-tier architecture. Its key design insight is to extend scalability in terms of consensus algorithm protocol, architecture, and scheduling. In particular, P3-Chain employs a dual-consensus algorithm with decoupled intra- and inter-shard operations, allowing them to run in parallel and asynchronously under practical scenarios. To resolve the conflicting transaction problem brought by this decoupled dual-consensus algorithm, P3-Chain incorporates a state-access locking mechanism. P3-Chain is implemented in Golang across multiple OSs, and it is evaluated on Hyperledger Caliper testbed, ensuring standardized and fair benchmarking. Through extensive experiments, the results indicate that P3-Chain can achieve TPS$3.3\times $that of FISCO,$3.3\times $that of partitioned FISCO,$2.7\times $that of Fabric,$2.3\times $that of AHL+,$2.2\times $that of SharPer and$7.4\times $that of Ethereum when system contains 32 nodes. Meanwhile, within the same experimental settings, P3-chain is scalable to 1024 nodes successfully, while Fabric and FISCO run with 64 nodes only. Furthermore, P3-Chain only sacrifice less than a 10% performance when the system scale expands$256\times $from 4 to 1024.
Abstract: Identity theft has emerged as a psychologically consequential form of cybercrime enabled by the proliferation of digital platforms, the expansion of datafication, and the collapse of traditional criminal–victim proximity. As personal identity becomes increasingly externalized through financial accounts, medical records, biometric templates, and algorithmically curated social profiles, offenders exploit cognitive biases, disclosure fatigue, and habituated oversharing to acquire and weaponize personal information. Criminal psychology research demonstrates that social engineering, authority mimicry, and emotional urgency manipulate victims into bypassing rational scrutiny, while cyberpsychology highlights the affective attachment individuals form with their digital representations. Unlike conventional theft, in which tangible objects are removed, identity theft appropriates informational components of the self, enabling prolonged impersonation, reputational distortion, and chronic anxiety that cannot be readily restored. Geographic detachment, encrypted communication channels, and anonymizing technologies reduce offenders’ perceived accountability, encouraged moral disengagement and facilitating mass victimization at minimal personal risk. Victims, confronted with unauthorized transactions or corrupted medical histories, report hypervigilance, loss of digital agency, and destabilization of narrative coherence. Emerging technologies, including Internet of Things devices, deepfake media, decentralized finance, and eventually quantum computing, further expand the attack surface and amplify criminogenic opportunity structures. Meanwhile, jurisdictional fragmentation complicates forensic attribution and legal recourse. Collectively, these developments reveal that traditional, place-based models of personal security are insufficient in networked environments. Safeguarding informational sovereignty requires interdisciplinary approaches that integrate behavioral criminology, cognitive vulnerability assessment, cyberpsychological resilience, and international policy coordination. Understanding identity theft as an ontological, relational, and psychologically persistent violation offers critical insight for prevention, victim support, and regulatory design in the digital epoch. Keywords: Identity Theft; Cyberpsychology; Criminal Psychology; Datafication; Digital Proximity Collapse; Social Engineering; Informational Sovereignty; Biometric Fraud; Cognitive Vulnerability; Cybercrime Scalability
Open access
2 source records
Cybercrime and Law Enforcement Studies
Crime Patterns and Interventions
Psychopathy, Forensic Psychiatry, Sexual Offending
Electronic voting systems have a perpetual dilemma in finding a harmonious balance between security, privacy, and fairness with the transparency and flexibility of modern digital technologies. Traditional voting systems are plagued with concerns of trustworthiness in the presence of centralized architectures, costly infrastructure, and tamperability. This work explains the dilemma of having a cost-effective and transparent voting process without compromising voter anonymity and data integrity. In search of a solution, we examine the use of blockchain as a service (BaaS) for the deployment of a distributed electronic voting system. The aim of this research is to conceptualize a tamperresistant voting infrastructure that is cost-effective and transparent. The proposed system leverages distributed ledger technologies to secure votes recording across decentralized nodes to ensure immutability and transparency. By performing a comparative study of existing blockchain platforms to determine the most suitable platform for large-scale deployment. The novelty of this work is the practical deployment of BaaS for the development of an electronic voting platform that is scalable, secure, and transparent, as demonstrated through a real-world election case study.
In the advancement of Industry 4.0, integrating intelligent systems, automation, and real-time data exchange has significantly transformed global industries. Among these developments, blockchain technology has emerged as a pivotal innovation, offering unparalleled data integrity, transparency, and decentralized control capabilities. This paper introduces a novel Ethereum-based decentralized application (DApp) architecture, comprising modular smart contracts to enhance industrial and societal systems, particularly within environmental sustainability in water body management. Each contract addresses a distinct operational layer, from secure storage and role-based access control to verified participation, drive coordination, and automated Ether-based reward distribution. The system was developed and tested in Remix IDE to validate functionality and integrity. This DApp framework secures workflows and extends blockchain utility to civic engagement through verified volunteer activities and incentive-based water conservation initiatives. Unlike prior work, this model integrates decentralized, scalable, and auditable mechanisms that have not previously been consolidated within a unified environmental DApp.
This study presents a novel decentralized and secure Automatic Optical Inspection (AOI) framework utilizing blockchain technology and smart contracts deployed on Nvidia Jetson edge computing devices to address integration complexities, security vulnerabilities, and excessive energy consumption in traditional AOI systems. The proposed architecture leverages Jetson devices as both processing units for image analysis and blockchain nodes, creating a decentralized network where inspection results are recorded through smart contracts. This approach ensures data immutability, transparency, auditability, and system resilience through decentralization. Experimental evaluation across three Jetson device models (Nano, Xavier NX, and Orin Nano Super) demonstrate that blockchain operations consume minimal resources. With the newer hardware Jetson Orin Nano Super showing average CPU usage of only 5.35% during blockchain operations (including ordering service), significant computational capacity remains available for AI-driven image inspection tasks. This enables the simultaneous execution of computer vision and AI recognition, as well as secure blockchain data recording, on a single edge device. The research implementing blockchain technology in resource-constrained edge devices proves the feasibility of blockchain-secured AOI in manufacturing environments.
Physical Unclonable Functions (PUFs) and Hardware Security
The theses examine the legal and financial aspects of cryptocurrencies, taxation specifics, and transaction monitoring.The advantages of cryptocurrencies in financial inclusion and blockchain implementation are outlined.Challenges of legal regulation and prospects for aligning Ukrainian legislation with EU MiCA standards are highlighted.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
This paper discusses the obstacles to the capitalization of data elements, such as the difficulties in confirming data ownership, trust deficit, privacy breaches, and inefficiency of transactions, through a distributed solution based on blockchain technology. First, a data ownership confirmation mechanism based on a consortium blockchain is established by using the Merkle tree and PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to achieve transparency and traceability of data ownership. Second, a multi-dimensional data value evaluation and RF-BP (Random Forest-Back Propagation) dynamic pricing mechanism are established by using machine learning algorithms to evaluate the value of data assets in a scientific manner. Third, a smart contract is established for pricing and payment, in order to achieve transaction automation and clearing and settlement. Finally, ZKP (Zero-Knowledge Proof) technology is applied to develop a mechanism for verifying compliance and privacy of data under the proposition of public review and "visible, invisible". Experimental results show that the proposed method reduces the average leakage risk and defense success rate under various attacks to 8.57 % and 97.1%, respectively. In terms of transaction efficiency, the proposed method achieves a throughput of 1250 TPS (Transactions Per Second) with a latency of 120 milliseconds at a 50-node scale. Overall performance is demonstrated with a confirmation and transaction success rate of 99.2% and 97.8%, respectively. The suggested framework provides reliable confirmation of data elements, scientific pricing, efficient trading and transaction processes, and privacy protection. It can support the vision of developing a secure, transparent and efficient market for data element circulation with technical feasibility and performance.
Modern cryptographic tools such as multi-party computation (MPC) and zero-knowledge proofs (ZKPs) offer strong, provable security guarantees—but these generic protocols remain impractical for production-scale machine learning (ML), especially in the era of large language models (LLMs). This thesis proposal advances the central claim that cryptographic protocols co-designed with the structure of specific ML subtasks can achieve practical efficiency without compromising privacy or verifiability. To validate this vision, this proposal develops three interconnected research thrusts: (1) Confidential Outsourced Training. Customized MPC protocols shift expensive cryptographic steps to local computations, enabling secure training of large models in untrusted clouds by resource-constrained data owners. (2) Scalable MPC Primitives for Large Datasets. Provably secure building blocks—such as oblivious shuffles, private joins, and sparse linear algebra routines—bridge the performance gap in privacy-preserving data pipelines at scale. (3) Verifiable ML without Retraining. Rather than proving each training step, a new proof-of-optimality framework certifies that a trained or fine-tuned model (e.g., LoRA adapters) satisfies desired properties, enabling efficient, auditable deployment without re-executing training. Together, these efforts aim to close the long-standing gap between privacy and efficiency, demonstrating that strong cryptographic guarantees and modern ML workflows can be reconciled through principled, application-aware design.
The increasing use of deep learning (DL) models has given rise to significant privacy concerns regarding training and inference data. To address these concerns, the community has increasingly adopted crypto-based privacy-enhancing technologies (CPET) like homomorphic encryption (HE), secure multi-party computation (MPC), and zero-knowledge proofs (ZKP). The integration of CPET with DL, often referred to as CPET-DL, is commonly facilitated by specialized frameworks like CrypTen, TenSEAL, and EZKL. These frameworks offer configurable parameters to balance model accuracy and computational efficiency during privacy-preserving operations. However, these configurations, while seemingly harmless, can introduce subtle vulnerabilities. The stealthy attacks induced by misconfigurations are hard to detect because 1) the plaintext models remain vulnerability-free, and 2) existing auditing tools are hardly applicable to CPET-hardened models. This creates a paradox: tools intended to protect privacy can be undermined through configuration manipulation.
Open access
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security