Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideal of censorship resistance, together with related fairness properties, is undermined in practice, motivating fairness mechanisms that aim to restore these properties. Several of these mechanisms hand control over block contents to a committee of proposers under a 1-of-n honest assumption: at least one committee member complies with the mechanism even when deviating would increase personal revenue. We refer to such proposers as altruistic. Yet prior work shows that roughly 91 percent of blocks are constructed by centralized block-building services that demonstrably take user-adverse actions for financial gain; the responsible proposers sign these blocks blindly, without any means of intervention. A common reading of this figure is that 9 percent of proposers forgo these gains and act altruistically. Our empirical analysis of the full year 2025 shows that this share is far smaller: at most 1.55 percent of proposers can plausibly be regarded as altruistic, whereas the remaining 98.45 percent of proposers exhibit observable non-altruistic behavior. We interpret 1.55 percent as an upper bound on the prevalence of altruistic proposers. These results imply that committee-based fairness mechanisms that rely on altruistic members would require substantially larger committees than currently proposed. This raises concerns about their practical viability and motivates mechanisms in which fair behavior is the rational choice.
Justin Wang, Andreas Bigger, Xiaohai Xu, Jiahao Lin · 8 authors
Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in ways that improve security and in ways that might increase risk. We introduce EVMbench, an evaluation that measures the ability of agents to detect, patch, and exploit smart contract vulnerabilities. EVMbench draws on 117 curated vulnerabilities from 40 repositories and, in the most realistic setting, uses programmatic grading based on tests and blockchain state under a local Ethereum execution environment. We evaluate a range of frontier agents and find that they are capable of discovering and exploiting vulnerabilities end-to-end against live blockchain instances. We release code, tasks, and tooling to support continued measurement of these capabilities and future work on security.
Siri Sanjana Pasunoori, Swetha U, Ravi Kanth Kotha, Kumar Dorthi · 7 authors
The increasing integration of distributed energy resources (DERs) into modern smart grids has created new challenges related to load balancing, real-time coordination, and secure energy transactions. Traditional centralized grid architectures are no longer sufficient to handle bidirectional energy flow, dynamic pricing, and operational requirements. The current paper proposes a scalable smart grid load balancing framework by integrating lightweight Message Queuing Telemetry Transport (MQTT) communication with a hybrid blockchain-based consensus mechanism. Practical Byzantine Fault Tolerance (PBFT) and Proof of Stake (PoS) were used to achieve consensus. MQTT provides low-latency and efficient communication among prosumer devices. And the blockchain layer ensures secure, tamper-evident, and auditable power transactions. The proposed hybrid consensus model achieves quicker transaction finality and byzantine fault tolerance within local microgrids and supports a scalable and economically secure environment through PoS. Smart contracts were utilized to automate important functions such as settlement, marginal pricing, and bid matching. The simulation outcome shows communication latency within a second, around 85% prosumer participation in demand response programs, and also a 23% increase in renewable energy utilization. The proposed framework provides a secure, transparent, and interoperable solution for next-generation decentralized smart grid systems.
In blockchain ecosystems, maintaining transparency and privacy has become an ethical dilemma. This is because, while certain specific information of the user is shared to ensure transparency of transactions across networks, such information could be detrimental to the user, as there is a possibility of it being tampered with. For instance, in the Catalyst voting process in Cardano, users can still see the amount of ADA tokens being held by other users, which can influence their voting options, especially when large ADA holders vote in support of certain ideas or proposals. To discourage such challenges as voter manipulation and vote buying, this study proposed the implementation of zero-knowledge proof (ZKP) in blockchain ecosystems to enhance the transparency of the catalyst voting process and enhance efficiency and speed of result release. Using survey questionnaire and a multivocal literature review, this study was able to proof that ZKP cannot only be applied in the catalyst voting process to enhance its transparency, but also addressed potential challenges to its applications such as scalability, encourage trust and fairness of the voting system, and improve voter participation due to its user-friendliness. Mathematical models emphasize scaled voting as optimal for balancing inclusion and plutocratic control.
When individual robots have limited sensing capabilities or insufficient fault tolerance, it becomes necessary for multiple robots to form teams during exploration, thereby increasing the collective observation range and reliability. Traditionally, swarm formation has often been managed by a central controller; however, from the perspectives of robustness and flexibility, it is preferable for the swarm to operate autonomously even in the absence of centralized control. In addition, the determination of exploration targets for each team is crucial for efficient exploration in such multi-team exploration scenarios. This study therefore proposes an exploration method that combines (1) an algorithm for self-organization, enabling the autonomous and dynamic formation of multiple teams, and (2) an algorithm that allows each team to autonomously determine its next exploration target (destination). In particular, for (2), this study explores a novel strategy based on large language models (LLMs), while classical frontier-based methods and deep reinforcement learning approaches have been widely studied. The effectiveness of the proposed method was validated through simulations involving tens to hundreds of robots.
The article provides a comprehensive study of the systemic transformation of corporate governance in the context of global digitalization, characterized by the transition from hierarchical models to decentralized structures. It is substantiated that blockchain technology emerges as a new institutional foundation, where traditional bureaucratic verification mechanisms are replaced by algorithms based on cryptographic protocols. A particular emphasis is placed on the distinctions between public (permissionless) and private (permissioned) blockchain networks regarding the immutability of records. The study examines the concept of decentralized governance and the functional specifics of Decentralized Autonomous Organizations (DAOs), where operational logic and management regulations are implemented directly into the software code of smart contracts. This minimizes the influence of traditional administrative management and mitigates "single point of failure" risks. The theoretical framework of the work builds upon classical theories, such as Oliver Williamson’s "Transaction Cost Theory," Michael Jensen and William Meckling’s "Principal-Agent Theory," and the scholarly works of Harold Demsetz. Blockchain is analyzed as a tool that renders market exchange more economically viable than hierarchy. The author proposes an original interpretation of a multi-tier blockchain model for enterprise management, encompassing the infrastructure, network, consensus, data, and application layers. The essence of consensus algorithms (PoW, PoS, DPoS) is disclosed through the prism of management. Special attention is devoted to international experience in legal regulation and the processes of implementing these standards within the legislative framework of Ukraine. The economic effect and practical aspects of the study are analyzed through successful case studies of global corporations (IBM, Amazon, Oracle, Walmart, Nestlé) and Ukrainian business initiatives (TASCOMBANK, SETAM, Agroxy, Softengi). These cases demonstrate a significant reduction in verification costs, lower operating expenses, and increased transparency in supply chains. The transition to an innovative "Management-as-a-Service" paradigm is justified, where blockchain serves not merely as software but as a new firm architecture. Conclusions are drawn regarding a shift in the management ontology – moving from "governance by humans" to algorithmic "governance by code," which ensures data immutability, cyber resilience, and the possibility of real-time preventive risk monitoring. References: 1. Kuzmina, T. O., Berezovskyi, Yu., Kalinskyi, Ye., Arliukova, Yu., & Trofymchuk, A. (2024). 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Sunil Kumar R M, Raghavendra M Devadas, Mrutyunjaya M S, Praveen Gujjar J · 6 authors
Healthcare data infrastructures remain fragmented, insecure, and characterized by low trust, hindering interoperable, patient-centric care. Centralized architectures struggle with multi-stakeholder governance, fine-grained accountability, and performance at scale under real-world workloads. Distributed ledger technology (DLT) promises immutability, distributed trust, auditability, and strong provenance, yet most healthcare implementations focus on narrow use cases, ad-hoc governance choices, or limited performance benchmarking. This paper designs and empirically evaluates an integrated governance architecture and performance engineering framework for healthcare DLT ecosystems. The approach combines (i) a theoretical synthesis of DLT governance and health data governance, (ii) architecture and protocol design for permissioned and hybrid platforms (e.g., Corda, Hyperledger Fabric, Ethereum-based frameworks, edge–blockchain hybrids), and (iii) testbed implementations with multi-scenario benchmarking across local, cloud, and edge-integrated deployments. Results from representative use cases—EHR interoperability, m-health/IoT streaming, and self-sovereign identity and consent—show that DLT-enabled governance can strengthen integrity, accountability, and patient control while achieving acceptable throughput–latency envelopes compared with centralized baselines, subject to explicit trade-offs between decentralization, audit granularity, and scalability. The paper distils design guidelines and reusable governance–performance patterns for large-scale, interoperable DLT health ecosystems, clarifying when to Favor platform choices, consensus mechanisms, and data-management strategies given domain-specific requirements.
Innovative Approaches in Technology and Social Development
Traditional financial institutions face inefficiencies that can be addressed by distributed ledger technology. However, a primary barrier to adoption is the privacy concerns surrounding publicly available transaction data. Existing private protocols for distributed ledger that focus on the Ring-CT model are not suitable for adoption for financial institutions. We propose a post-quantum, lattice-based transaction scheme for encrypted ledgers which better aligns with institutions' requirements for confidentiality and audit-ability. The construction leverages various zero-knowledge proof techniques, and introduces a new method for equating two commitment messages, without the capability to open one of the commitment during the re-commitment. Subsequently, we build a publicly verifiable transaction scheme that is efficient for single or multi-assets, by introducing a new compact range-proof. We then provide a security analysis of it. The techniques used and the proofs constructed could be of independent interest.
Este relatório de pesquisa investiga a aplicação metodológica da analogia da força centrífuga ao campo da ciência econômica, com foco especial na dispersão de capital, renda e agentes em ambientes de alta volatilidade e inovação tecnológica. Através da construção do <i>Economic Centrifugal Dispersion Model</i> (ECDM), o estudo analisa como o influxo de capital () e a velocidade das transações (), ponderados pela resistência regulatória e institucional (), determinam a expansão ou a contração de mercados. A tese central sustenta que os sistemas econômicos contemporâneos, especialmente aqueles fundamentados em tecnologias Web3 e <i>tokenomics</i>, operam em ciclos de centralização-expansão que podem ser modelados matematicamente como sistemas rotacionais físicos. O relatório integra teorias da Nova Geografia Econômica de Paul Krugman, a praxeologia de Ludwig von Mises, o Efeito Cantillon e a Teoria do Caos para explicar a migração de valor do centro para a periferia. Utilizando evidências de teoria da organização, capital humano e dinâmica de redes, conclui-se que o ECDM oferece uma ferramenta preditiva robusta para identificar bolhas especulativas, processos de desintermediação e reequilíbrios de mercado em DAOs e sistemas financeiros descentralizados.<br>
We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward looking and rolling evaluations on Bitcoin transaction data, we show that strong static classification metrics substantially overstate real world regulatory effectiveness. Temporal nonstationarity induces pronounced instability in cost sensitive enforcement thresholds, generating large and persistent excess regulatory losses relative to dynamically optimal benchmarks. The core failure arises from miscalibration of decision rules rather than from declining predictive accuracy per se. These findings underscore the fragility of fixed AML enforcement policies in evolving digital asset markets and motivate loss-based evaluation frameworks for regulatory oversight.
The rapid digitalization of wealth in the form of cryptocurrency and virtual assets has dramatically transformed the results of the matrimonial conflicts and alimony payments. With the gr owing adoption of decentralized and pseudonymous digital assets as constituents of individual financial portfolios, family courts face new issues in their classification, disclosure, valuation, and enforcement. The legal issues discussed in this paper include the legal complications of cryptocurrency as marital property, the risk of concealing assets through blockchain anonymity, and challenges of valuation, associated with the excessive price volatility, tax exposure, enforcement challenges linked to the control of private keys, and jurisdictional challenges across borders. It also examines new legal and forensic systems and contractual protection mechanisms that are intended to manage these issues. The paper claims that although the classical tenets of equitable allocation and full disclosure are still underpinning, the concept of clarity in the law and judicial flexibility is needed to provide equal justice, openness, and enforceability of the divorce process concerning cryptocurrency and virtual possessions.
Byzantine Fault Tolerance (BFT) has developed from a theoretical concept in distributed database reliability to the basic structure of today's decentralized finance and global infrastructure. This paper provides a holistic overview of BFT approach, delineating imperative strides from classical synchronous resolutions to the most recent blockchain protocols. We give a formal treatment of the transition of the architecture from quadratic complexity (O(n2)) in PBFT to linear scalability$(O(n))$in HotStuff and probabilistic guarantees, as compared with Nakamoto Consensus. Moreover, we present a critical review of some recent developments in 2024 and 2025 related to Machine Learning-accelerated adaptive consensus, probabilistic relaxation for high volume trading, lightweight protocols for IoT. By combining theoretical limits with a range of practical issues such as state transfer and cryptographic overhead, we hope that the survey provides a structured roadmap for researchers tackling the scalability-security trade-offs in future distributed systems.
Zero-knowledge proof security rests on cryptographic reductions: breaking a ZK scheme requires breaking an underlying hard problem. We introduce an independent, complementary security analysis based on the Structural Action Principle. We extend the discrete action functional S[psi] = sum_t lambda(s_t) from Boolean CDCL trajectories to algebraic constraint systems over finite fields F_p, defining an Algebraic Structural Action with density functions that recover Groebner basis complexity, Polynomial Calculus proof size, algebraic degree growth, and elimination ordering as mechanical analogues. We prove a non-circular lower bound for preimage search in substitution-permutation network (SPN) hash functions: for a k-round SPN with S-box degree alpha and state width t, the preimage search system is a square polynomial system of k*t degree-alpha equations in k*t variables. Under the semi-regularity assumption (standard in algebraic cryptanalysis, empirically verifiable, and independent of any cryptographic security conjecture), the solving degree d_reg is determined by the Hilbert series H(z) = (1 - z^alpha)^{k*t}/(1-z)^{k*t}. We establish two action bounds: a peak bound S[psi] >= d_reg (any trajectory must encounter degree d_reg), and a stronger cumulative bound S[psi] >= sum_{d=alpha}^{d_reg-1} h_d using the Hilbert function coefficients as density, which captures the total algebraic work rather than just the peak degree. For Poseidon (alpha=5, t=3, k=8): d_reg = 97 and the cumulative bound gives S[psi] >= 2^{56}. The result applies to ANY SPN hash function (Rescue, Griffin, Anemoi, MiMC) and provides a second line of defense for Behavior-Bound Signature (BBS) security, grounded in algebraic proof complexity rather than crypto- graphic hardness assumptions. Keywords: structural action principle, algebraic proof complexity, polynomial calculus, semi-regularity, Hilbert function, SPN hash functions, zero-knowledge security, behavior-bound signatures
D. K. Shareef, Shaik Abdulla, Pulagari Maruthi Prasad, Paseddula Ajay · 5 authors
Supply chain finance (SCF) is an important tool in maintaining liquidity, confidence, and business continuity in the multi-stakeholder supply networks. Nevertheless, traditional SCF systems have weak real time inventory tracking, centralized trusting, transaction settlement lag and high vulnerability to fraud. This paper suggests an intelligent supply chain finance model based on the Blockchain -IoTdriven supply chain model by incorporating real-time inventory monitoring, secure decentralized transaction management and predictive decision support, to overcome such limitations. IoT sensors keep an eye on the level of inventory and environmental conditions, and blockchain technologies provide immutable, transparent, and resistant to alterations financial records on the form of smart contracts. A predictive analytics module is developed based on a Long Short-Term Memory (LSTM) that predicts the inventory demand and financial risk fluctuations to enable proactive decision-making. An interactive dashboard consolidates real-time and predictive knowledge to be able to make automated and data-led financial decisions. Experimental assessment proves that the developed framework has 95% accuracy when inventory, time and cost of transactions are significantly lowered, fraud cases are reduced, and the accuracy of demand forecasting is enhanced. This study validates the idea that IoT-blockchain-predictive analytics can offer a scalable, secure, and intelligent solution to next-generation supply chain finance systems.
Pellakuri Vidyullatha, R. Sreejith, Amjad Ali Syed, Sanjeev Kumar · 5 authors
Digital identity in healthcare has evolved from a convenience into a necessity, yet its dependence on centralized authentication continues to expose systems to privacy breaches and operational fragility. Existing identity models, though secure in principle, often collapse under real-world conditions where IoT devices, patient data streams, and network failures coexist. Most frameworks optimize for privacy or performance but rarely both. This study proposes a Resilient Privacy-Preserving Digital Identity Framework (RePP-DIF) that fuses artificial intelligence (AI), Internet of Things (IoT), and blockchain to achieve adaptive and fault-tolerant authentication within healthcare networks. The framework integrates a CNN–LSTM edge predictor for anomaly detection, zero-knowledge proofs for selective credential disclosure, and a replica consensus mechanism to sustain verification during validator failures.
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
S. Senthilkumar, M Alex Pandian, B Linu Harish, V Harish
Blockchain has recently attracted significant attention, particularly for its potential to address major issues in traditional electronic voting such as limited transparency, centralized control, and vulnerability to tampering. In this research, it aimed to design and evaluate a blockchain-based electronic voting system that ensures voter privacy, increases transparency, and can efficiently manage large-scale elections. The proposed system adopts a modular, layered architecture featuring secure voter registration, authenticated vote casting, automated tallying, and public auditing. It operates on a permissioned blockchain, with smart contracts enforcing the necessary rules and validations. To maintain security, the system incorporates public-key encryption, cryptographic hashing, zero-knowledge proofs, and threshold cryptography. This combination guarantees ballot confidentiality, integrity, and non-repudiation for voters. For consensus, the system utilizes Practical Byzantine Fault Tolerance (PBFT). To evaluate performance, the conducted simulations that measured transaction latency, voting throughput, and scalability as participation increased. The findings revealed low latency, consistent throughput, and strong scalability, making the system suitable for both national-scale elections and smaller voting scenarios. In comparison to conventional e-voting platforms, this blockchain-based approach eliminates single points of failure, significantly reduces the risk of vote manipulation, and enables transparent auditing of the election process.
The rapid rise in cryptocurrencies has created an investment environment marked by unprecedented levels of information volume, fragmentation, and volatility. While prior research has examined drivers of trust and adoption in crypto markets, far less is known about the psychological consequences of information overload on investor decision-making. This study addresses this gap through nineteen semi-structured interviews with individual cryptocurrency investors, analyzed using an inductive, manually conducted thematic approach. Findings reveal four interconnected dynamics: decision fatigue and paralysis, heuristic reliance on influencers and peers, emotional strain characterized by anxiety and fear of missing out (FOMO), and diverse coping strategies ranging from selective filtering to withdrawal. These results demonstrate that crypto investing is not only a financial process but also a cognitively and emotionally taxing experience. By linking investor narratives to broader theories of decision fatigue, bounded rationality, and consumer vulnerability, the study contributes to interdisciplinary debates in marketing, behavioral finance, and consumer psychology. Practically, the findings highlight the need for clearer communication strategies, supportive platform design, and financial education initiatives that help investors manage cognitive strain and decision fatigue. In a market where credibility is fluid and decisions are often made under conditions of overload, understanding the psychological dimensions of investment behavior is essential.
The current trends in the cyber threat landscape of distributed systems have required a paradigm shift to decentralized and thrustless security. The research suggests a new architecture, Federated Adversarial-AI for Zero-Trust Explainable Cybersecurity (FAZTEC), combining federated learning and adversarial artificial intelligence to help make the cybersecurity systems more resilient, and explainable. The proposed framework, with the help of federated learning, would allow interconnected threat detection on edge devices, which does not require sharing raw data since it would keep privacy and meet the criteria of regulatory requirements. The same happens through the use of adversarial AI in order to simulate advanced attack scenarios and thus strengthen the defines mechanisms of the threats that are evolving. Auditioning explainable AI (XAI) modules also increases transparency in the system, where the security analyst can understand and verify the detection results in real-time. The zero-trust architecture also verifies a device, user, and data flow continuously, which discards the implicit assumptions about trustworthiness. A wide range of experiments performed in various network environments proves the effectiveness, validity, and interpretability of FAZTEC, which represents a serious breakthrough in proactive cybersecurity protection. The work is useful to the future of security infrastructure, which is smart, decentralized and explainable, and applicable to critical applications in finance, healthcare, and government.
Aravinda S. Rao, Babu Pillai, Marimuthu Palaniswami, Vallipuram Muthukkumarasamy
Global financial integrity is fundamentally challenged by cryptocurrency mixers such as Tornado Cash, which facilitate billions in illicit fund flows. Low detection rates, reliance on labeled training data that is unavailable for novel attacks, and failure to analyze temporal coordination patterns are all impediments to the effectiveness of existing forensic tools. We introduce CONSENSUS, a self-supervised heterogeneous ensemble framework that addresses the challenge of attribution in mixed transaction streams. Our system requires no pre-existing labels, and it generates supervision signals directly from on-chain behavioral patterns. It synthesizes evidence by orchestrating nine analytical modalities—including deterministic clustering, behavioral analysis, and multiple graph neural network architectures—through a formal consensus mechanism. This multi-modal approach produces transparent, auditable risk scores from a 111-dimensional behavioral fingerprint. We validated the framework on five major decentralized finance (DeFi) exploits, including the Ronin Bridge and Poly Network hacks. Using raw transaction data, it detected all known primary attackers at 100% accuracy without training. Crucially, the framework's self-supervised components successfully identified the novel attack pattern of the Poly Network exploit, thereby demonstrating robustness to out-of-distribution threats that defeat supervised methods. By providing a transparent, zero-label solution, CONSENSUS establishes a new paradigm for flexible, effective risk profiling and forensic investigation.
The city of Cluj-Napoca turned into the biggest real estate boom in Romania. Although wages have remained at the national average level by field of activity, the price of housing tends to take such a large scale that it exceeds the amount of real estate in many European countries and cities. The community of ordinary, honest and industrious people sees themselves excluded from their own city, suffering because of this price explosion which has an impact in all social spheres. The explanation that the price level is due to the large number of students and computer scientists is easy and convenient for the authorities who do not really have reactions, answers and solutions. KEYWORDS: accommodation, maximum profit, corruption, computer scientists, real estate, money laundering, indolence, incompetence, complicity, community suffering, emigration, solutions. J.E.L. Classifications: R31, R23, O18 1. ARGUMENT After December 1989 in Cluj-Napoca there were phenomena and facts prominently highlighted on the national socio-economic map: the Caritas pyramid megagame, the FNI scam guaranteed in the end by the CEC, the bankruptcy of the largest private Bank "Dacia Felix", the headquarters of two antagonistic national parties UDMR and PUNR and very important, in the long term, the expansion of "Babeș Bolyai" University which became the largest in the country, both in terms of number of students and as the number of sections. The transformation of the number of students into an economic, not only scientific, cultural and social argument, even without coverage on the labor market, induced the increase in the number of students at all universities. It is not easy to mention "all" universities! The six state universities have a large share: "Babeș Bolyai" University, Technical University, University of Medicine and Pharmacy, University of Agricultural Sciences and Veterinary Medicine, University of Art and Design, Academy of Music. Along with these, the legislation after 1989 allowed the establishment of private education - "Bogdan Vodă" University (one of the first in the city), Dimitrie Cantemir University (centered in Bucharest, but with strong branches of Law and Economic Sciences in Cluj), "Sapientia" University focused on the Hungarian community, as well as "Partium" University in Oradea which has activity in Cluj as well, as well as other higher education institutions reorganized/disbanded over time, or with more limited or meteoric activity - "Avram Iancu", "Spiru Haret", "Phoenix", the Protestant Theological Institute and we do not claim to list exhaustively. It is certain that Cluj-Napoca has the highest density of students compared to the number of inhabitants in the country. These crowds of students, about 100 thousand with master's and doctoral students, in principle, were mainly charged tuition fees and all kinds of expenses were increased, based on the well-known principle of the price that appears as a result of the competition between demand and supply. There is a very high demand in Cluj, sometimes exorbitant, the solution, the most profitable and immediate, was just to increase the prices. Given that practically no dormitories were built after 1989, (only one in the Gheorghieni District, near "Economic Sciences" - FSEGA, but it is not entirely dedicated to students) among the prices that have exploded in Cluj, the shocking is that of rents and, logically, in the next steps, real estate prices. The explanation of the large number of students and IT specialists is the most convenient in excusing apathy or anti-crime inefficiency, but it is also necessary to analyze the hypothesis if part of the pressure on prices can come from financial flows associated with organized crime. 2. POSTULATED: ORGANIZED CRIME IS INTERESTED BY THE BIGGEST PROFIT The accommodation capacity in the state dormitories is approximately 14,000-15,000 places, and the students who do not get a place in the dorm, volens-nolens, enter a rental market where a level of 300-500 euros for a studio apartment excludes young people from disadvantaged backgrounds from Cluj university studies. Many of them would have deserved to perform in Cluj! It is estimated that approximately 65,000 students live in rented accommodation annually. Most students barely pay their rent and living expenses, very few can afford to enter the property market as buyers. There is, however, one category that influences the real estate market the most - IT specialists. They influence more because they have high salaries relative to the rest of the population. However, the infusion of students and the university environment determined the explosion of the crowd of IT-scientists in Cluj-Napoca, rightly considered a "Silicon Valley" of Eastern Europe. The estimate goes up to the existence of about 30,000 IT specialists in Cluj with a number of over a thousand active IT companies. All of these provide clues to the size of the rental market and the total value of real estate transactions. In 2025, Cluj county registered an approximate volume of 30,782 real estate transactions with an estimate of between 1.8 - 2.2 billion EUR annually (sources: ANCPI - National Real Estate Agency; Imobiliare.ro; Storia.ro s.a.). The rental market is more difficult to quantify, but it can be approximated by the number of residential units estimated to be in the rental circuit of at least 45-50 thousand units (apartments and rooms). Considering the data published in some specialized websites, Imobiliare.ro; Storia.ro, or of public institutions (City Hall of Cluj Napoca - floating population) we arrive at a total estimated annual value of approx. 300 million EUR. What happened in the USA during alcohol prohibition when alcohol smuggling produced a huge amount of black money? Who appeared on the market? In drugs, in human trafficking, in the smuggling of oil to Yugoslavia in the 90s, in the massive cutting of forests in countries that do not protect them, in prostitution, in gambling and betting, wherever, when the stake of a very large profit appears, even if it is illegal, who undoubtedly appears? Popular wisdom has an expression that captures the phenomenon: "Let it be, because frogs gather!" Is there a risk that part of the real estate market in Cluj or in Romania will be accessed, influenced or even controlled by organized crime? The clearest proof of the influence of organized crime is the huge number of homes sold that remain unoccupied! Between 18,000 and 24,000 housing units, depending on the information sources, in Cluj-Napoca alone. Thousands of apartments are bought for hoarding. Rising property prices coupled with very low interest rates offered by banks on savings have made buying an additional home an investment for anyone who can afford it. But it is also a classic method of money laundering where the goal is not the profit from the rent, but the legalization of the initial amount through subsequent resale. REAL ESTATE IS FAVORITE TO BE A "SAFE HAVEN" (SAFE REFUGE) FOR ILLICIT CAPITAL Although the presence of the IT sector and students would lead to the thought of a constant demand, especially in the conditions in which a salary recession in IT is foreseen (it actually took place), the hyperbolic evolution of real estate prices in Cluj cannot be justified. Practically, these real estate prices have been decoupled from the real purchasing power of the average salary, even if this "average", in Cluj, contains a lot of IT. Who does the disconnection? A working student, a programmer even with an above-average income is subject to the bank lending grids. When the price per square meter exceeds the threshold of 3000 euros/sq m, or in special areas/center, over 5000 euros/sq m, they become unaffordable for the middle class through mortgage credit. Everyone knows that high prices are supported with "cash", which highlights sources of financing external to the transparent banking system, specific to organized crime that needs to "clean" financial funds of dubious origin as quickly as possible. It's like a geometric law, a postulate, it's just like that, but even if it's like that, it's certainly not a proof on file, it's just an assumption. There are institutions that, based on some laws, will look for this evidence, being within their competence. Some authors on economic crime suggest that real estate markets in fast-growing cities may become vulnerable to the infiltration of capital from illicit activities (Unger, 2021). The real estate sector is recognized as a classic instrument for money laundering, including the profits from drug trafficking (FATF, 2019). An interesting work in the field is by a collective led by Klitgaard Robert "Corrupt Cities: Practical Guide to Institutional Reform". These specialists identify three main mechanisms: money laundering through real estate investments because real estate offers: high and relatively stable value; the possibility of justifying the origin of the funds; integration into the formal economy. capital reinvested from the drug market. According to the UN Office on Drugs and Crime (UNODC), the drug market generates hundreds of billions of USD annually globally (UNODC, 2023). Part of these funds are reinvested in: residential real estate; commercial premises; urban developments. In Europe, studies on cities such as Amsterdam or Barcelona have shown correlations between the underground economy and speculative real estate investments (Savona, 2020). the third mechanism refers to demand distortion. Is it the case of Cluj? preservation of value; anonymization of property; integration into the legal circuit. Thus, artificial demand can push prices above the level determined strictly by legitimate supply and demand. COMPETENT INSTITUTIONS, CASE STUDY AND THE THREAT NARCO TRAFFIC Combating the phenomenon would require the coordinated action of several institutions: Directorate for the Investigation of Organized Crime and Terrorism (DIICOT) National Anticorruption Directorate (DNA) National Office for the Prevention and Combating of Money Laundering (ONPCSB) National Fiscal Administration Authority (ANAF) General Inspectorate of the Romanian Police (IGPR) Law no. 656/2002 on the prevention and sanctioning of money laundering Law no. 143/2000 on preventing and combating drug trafficking and illicit drug consumption Criminal Code (art. 367 – organized criminal group) Examples of relevant files: Romania 2020 – DIICOT file on drug trafficking and money laundering through real estate purchases in Bucharest (DIICOT public release); 2022 – File regarding an organized criminal group involved in tax fraud and real estate investments (Bucharest Court of Appeal), 2023 – Case instituted by DIICOT regarding cocaine trafficking and property investments in the west of the country. Germany: Investigations coordinated by the Bundeskriminalamt (BKA) demonstrated the use of real estate for the recycling of profits from drug trafficking networks (BKA Report, 2021). Netherlands: The Financial Intelligence Unit (FIU Nederland) reported in 2022 the increase in suspicious transactions in the real estate sector in Amsterdam, associated with the drug economy. THE THREAT OF DRUG TRAFFICKING ON CLUJ. INCLUSIVE OF CLUJ REAL ESTATE Cluj-Napoca is a major university center, and the reports of the National Anti-Drug Agency indicate the existence of high consumption in the university environment (ANA, 2022). In economic theory, a city with: high consumption, logistical proximity, high purchasing power, can generate important financial flows in the underground economy. If these flows are reinvested in real estate, it results: additional pressure on demand; cash purchases; lack of price sensitivity. However, we note that no public data has been identified that accurately quantifies the weight of this phenomenon in the formation of prices in Cluj-Napoca. 6. CONCLUSIONS AND PROPOSALS It is well known that the upward spiral of real estate has a major negative impact on all components of social life. Institutions with competences in the field are expected to leave their mark more and contribute to the normalization of the perspectives of local communities, despite a lack of social reactivity typical of an increasingly aggressive, apathetic population, resigned to the idea that nothing can be done to improve living conditions. A lot can still be done, there are international anti-corruption models. Where there was political will, things got better, some negative phenomena were even eradicated, and some proposals can be extracted from those models: Extending the verification of the source of funds for transactions above a certain value threshold. Constantly checking the real prices from the real estate agencies with those declared at the notary chambers: Automatic interconnection ANAF–ONPCSB–DIICOT. Complete public register of beneficial owners (in line with EU Directive 2018/843). Extended confiscation according to art. 112 Criminal Code. Romania is the country that in peacetime was condemned by pauperization, lack of perspective, systemic corruption to have the largest share of emigrant population in Europe, and of course with an unwanted leading place in the world, and all this took place in peacetime. By analogy, from the much-acclaimed "5-star city", the aberrant price spiral in Cluj-Napoca can produce similar, uncontrollable effects internally. Paraphrasing the legal admonition, we would conclude by warning, "Any silence can turn against us!" REFERENCES National Antidrug Agency (2022). National report on the drug situation in Romania. Balan, C. (2023). Urban economy and real estate market. ASE publishing house. Bundeskriminalamt (2021). Organized Crime Situation Report. Financial Action Task Force (2019). Money Laundering & Real Estate FIU Nederland (2022). Annual Report on Suspicious Transactions. Glaeser, E. (2011). Triumph of the City. Penguin Press. Klitgaard Robert s.a "Corrupt Cities: Practical Guide to Institutional Reform" Ed. Humanitas, Bucharest 2012 Marian Adrian Sorin, s.a THE STUDENT'S GUIDE, Mega Publishing House, Cluj-Napoca, 2016, Marian Adrian Sorin, "Why do Romanians emigrate?" Galaxia Gutenberg Publishing House, Cluj-Napoca, 2023, Marian Adrian s.a, Competences and milestones of training and cooperation in the public order and safety system Galaxia Gutenberg Publishing House, Cluj-Napoca, 2021 Savona, E. (2020). Organized Crime in European Cities. Springer. Unger, B. (2021). The Role of Real Estate in Money Laundering. Journal of Financial Crime. United Nations Office on Drugs and Crime (2023). World Drug Report. ***Law no. 656/2002 for the prevention and sanctioning of money laundering
The classic digital divide theory asserts that unequal access to and unequal experience with information technologies may lead to unequal user outcomes. This paper introduces a new perspective to extend this theory: outcome divides can persist despite equal access and equal experience if users differ in their analytical ability to analyze and interpret available data for decision-making. We term this new data-to-decision skill as analytical ability and integrate it into the classic digital divide framework. We develop a new approach to operationalize analytical ability by contrasting humans’ actual performance against that of a standard machine learning model that makes similar analytical decisions based on the same information available to humans, essentially emulating a quasi-random counterfactual setting. To minimize the confounding impact of other divides, we validate the role of analytical ability in information-transparent environments like the blockchain-based trading markets, where all historical trading data is equally available to all users on the blockchain. We leverage data from EnjinX, a blockchain-enabled non-fungible token (NFT) marketplace that records all historical NFT transactions. We measure user outcomes by their flip trading performance, a standard metric captured via the percentage of exploited flipping opportunities. Our empirical analysis reveals that disparities in analytical ability may become the new bottleneck for outcome equity: flip trading performance could decrease by 66.86% when traders are incapable of analyzing the available blockchain information effectively. Our study contributes to the literature by extending the digital divide theory with the notion of the analytical ability divide. Moreover, we are among the first to rigorously quantify analytical ability and empirically test its impact based on the extended digital divide framework. Our study also offers important practical implications for platforms and policymakers to bridge this new divide in order to foster outcome equity.
Antonio Pérez de Juan, Íñigo Martín Melero, Raúl Gómez-Martínez, María Luisa Medrano-García
This study investigates the relationship between public attention to the Sustainable Development Goals (SDGs) and cryptocurrency demand, specifically for Bitcoin (BTC) and Cardano (ADA). Given the environmental concerns associated with Proof-of-Work (PoW) and the sustainability benefits of Proof-of-Stake (PoS), we hypothesize that increased SDG attention leads to higher demand for green cryptocurrencies like Cardano and lower demand for non-green cryptocurrencies like Bitcoin. Using Ordinary Least Squares (OLS) regression and supervised machine learning algorithms, we analyze weekly cryptocurrency returns and Google Trends data from 2020 to 2025. The findings suggest that SDG attention has a statistically significant but weak negative impact on Bitcoin returns, while no significant effect is observed for Cardano. Machine learning models fail to predict cryptocurrency demand effectively. These results indicate that sustainability awareness alone is not a primary driver of cryptocurrency investment behavior.