Third-party Entities Research Articles

State-sponsored cyber attacks and co-movements in stock market returns: evidence from US cybersecurity defense contractors

Abstract State-sponsored cyber attacks are increasingly attracting attention in the literature, with many analysts interested in the firm-level economic implications of these attacks. Nevertheless, the bulk of these studies focus on firms directly targeted in the attacks. In this paper, I examine the broader, often overlooked ripple effects of these attacks on third-party entities like cybersecurity service providers who are frequently at the frontlines of dealing with these attacks. Leveraging data on cyber attacks and stock market returns for a sample of U.S. based cybersecurity defense contractors from 2000 to 2020, I empirically demonstrate that an escalation in the intensity of state-sponsored cyber attacks prompts a behavioral shift among investors and regulators, leading to increased co-movement in firms’ stock returns. This paper thus adds a novel dimension to our understanding of the complex interplay between state-sponsored cyber attacks and the market dynamics of cybersecurity defense contractors, with important implications for national cybersecurity.

Cryptocurrency and its Nexus with Money Laundering and Terrorism Financing within the Framework of FATF Recommendations

Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.

Waste Lubricant Oil Management: A Review of Technical Aspects and Regulatory Compliance

PT X, one of the largest coal mining companies in Indonesia, is located in Berau District, East Kalimantan. The company's mining activities produce waste lubricant oil, a dominant hazardous waste containing toxic materials that can significantly pollute the environment if not properly managed. Given the critical importance of managing waste lubricant oil, Indonesia has established several regulations for hazardous waste management. This study aims to evaluate both the technical aspects and regulatory compliance of PT X's waste lubricant oil management by comparing existing practices with technical criteria and applicable regulations. The methodology involves assessing the capacity of hazardous waste storage facilities based on the generation of waste lubricant oil, regulatory requirements, and transportation frequency, alongside compliance assessments of packaging, storage, and in situ utilization practices. Results indicate that PT X's storage capacity for waste lubricant oil is adequate and that its internal management practices fully comply with regulations. However, nonconformities were found in the hazardous waste management practices of third-party entities, particularly in packaging and storage. This research highlights the need for improved monitoring and regulation of third-party waste management practices to ensure comprehensive compliance and environmental protection.

An Analysis of the Influence of ESG Rating Divergence on Investor Sentiment

The global financial markets have experienced a significant development with the emergence of the notions of "sustainable development" and "green investing". This development involves the integration of Environmental, Social, and Governance (ESG) information into investment decisions. A significant advancement in the worldwide financial market is the integration of ESG (Environmental, Social, and Governance) data into investing choices. ESG ratings have emerged as a significant benchmark for investors in this scenario. However, there is now a considerable disparity in the rating outcomes provided by various rating agencies for particular companies. This disparity distorts the ratings and thus triggers exaggerated reactions from investors. This study investigates the influence of differences in ESG ratings on investor sentiment and its underlying mechanism using a sample of businesses listed on the A-share market in Shanghai and Shenzhen between 2015 and 2022. Research has shown that when there is a significant difference in ESG ratings, it has a detrimental effect on investor sentiment. This means that when there is a large divergence in ESG ratings, it negatively affects investor sentiment, leading to a decrease in their evaluation and confidence in the company. Empirical investigations demonstrate that the focus and consideration given by analysts and research reports play a crucial role in how differences in ESG ratings impact investor sentiment. Additional investigation reveals that the adoption of GRI standards and the verification of ESG reports by third-party entities can somewhat reduce the influence of divergence on the fluctuation of investor mood, as well as enhance investor acknowledgment of revealed information. The paper examines the influence of disagreement in ESG ratings on investor mood, contributes to existing research on ESG ratings dispute, and presents empirical evidence to support the development of China's ESG ratings system and the establishment of a "rational" investment market.

Secure cross-chain interaction solution in multi-blockchain environment

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.

Defense Against On-Chip Trojans Enabling Traffic Analysis Attacks Based on Machine Learning and Data Augmentation

Modern computing systems involve huge data exchange across various sections of the processing system. To facilitate this, Network-on-Chip (NoC) serve as a crucial infrastructure that connect the processing cores to memory, peripherals, etc. The system could be put at great risk should the NoC system become compromised. The NoCs are used in multi/many-core processors; this domain is experiencing increased threats because of Hardware Trojan (HT) embedded in the multi core processing systems due to the presence of third-party entities in the system-on-chip (SoC) design pipeline. Protecting user and system level privacy becomes important in such multi core systems to enable trust. By embedding a hardware Trojan (HT) in a NoC, the adversary can snoop on important insights regarding the applications executing on the system or the user profile information. An attack of such calibre can compromise privacy thereby enabling more advanced attack on the entire system. This work demonstrates the capability of a traffic analysis attack when a few HTs are embedded in the NoC switches of a multi/many-core processor. The attack is capable of exposing sensitive information to an external malicious attacker who can then analyze the payload data with sophisticated machine learning (ML) techniques to infer the applications executing on the system. We also evaluate the performance of a generative adversarial network (GAN) strengthened attacker model that offers more robustness for data paucity scenarios. We propose a Simulated Annealing-based randomized routing algorithm based defense for NoCs thus thwarting the attack. The results demonstrate that the proposed randomized routing algorithm could reduce the accuracy of identifying user profiles by the attacker from >98% to <15% in multi/many-core systems.

An Analytical Review of Industrial Privacy Frameworks and Regulations for Organisational Data Sharing

This study examines the privacy protection challenges in data sharing between organisations and third-party entities, focusing on changing collaborations in the digital age. Utilising a mixed-method approach, we categorise data-sharing practices into three business models, each with unique privacy concerns. The research reviews legal regulations like the General Data Protection Regulation (GDPR), highlighting their emphasis on user privacy protection but noting a lack of specific technical guidance. In contrast, industrial privacy frameworks such as NIST and Five Safes are explored for their comprehensive procedural and technical guidance, bridging the gap between legal mandates and practical applications. A key component of this study is the analysis of the Facebook–Cambridge Analytica data breach, which illustrates the significant privacy violations and their wider implications. This case study demonstrates how the principles of the NIST and Five Safes frameworks can effectively mitigate privacy risks, enhancing transparency and accountability in data sharing. Our findings highlight the dynamic nature of data sharing and the vital role of both privacy regulations and industry-specific frameworks in protecting individual privacy rights. This study contributes insights into the development of robust privacy strategies, highlighting the necessity of integrating comprehensive privacy frameworks into organisational practices for improved decision making, operational efficiency, and privacy protection in collaborative data environments.

Taxation Of Permanent Establishments In Epc Contracts The Emerging Trends, Rulings, And The Way Forward- Issues Arising And Judicial Views Under Direct Taxes -An Indian Perspective

The importance of taxation of EPC contracts is critical from the point of view of both the Indian economy, for the contribution it makes to economic growth and development. An EPC contract is composed of two parts Activities done outside the country and Onsite- performed within the country. It involves the participation of many international players, who bring different skill sets, with a separate specific scope, woven into an umbrella contract for the final delivery of a project to the customer, there is a tendency for the taxman to try and tax the contract – treating it mainly as a single, turnkey contract, while the taxpayer tries to reduce the outcome of taxable income. The various motives involved on the part of the taxpayer to reduce the obligations on tax liability to a minimum level, using different arrangements, structures, sub-contracts, split contracts to subsidiaries, and third-party entities in developing countries are carefully thought out using special tax counsel. The study is exploratory and also examines the past studies in motives, key factors influencing the structure of entity to sub-contract, and rulings by the judiciary at various forums like ITAT, High Court, and Supreme Court and examines the various factors which had determined the decision, as to how it will influence the future of taxation of EPC contracts, with post-BEPS effects and the economy going digital too. The cases involving EPC contracts, the different types of PEs arising in the process, motives to save taxes, reasons for employing SPV structures, the role played by governance, tax counsel and then examine the court cases in the Indian scenario, and draw conclusions as to how it will be going forward in future.

Tripartite Evolutionary Game and Simulation Analysis of Healthcare Fraud Supervision under the Government Reward and Punishment Mechanism.

This study aims to provide useful insights for the Chinese government in dealing with healthcare fraud by creating an evolutionary game model that involves hospitals, third-party entities, and the government based on the government reward and punishment mechanism. This paper analyzes the evolutionary stability of each participant's strategy choice, discusses the influence of each element on the tripartite strategy choice, and further analyzes the stability of the equilibrium point in the tripartite game system. The results show that (1) the government increasing fines on hospitals is conducive to compliant hospital operations, and the incentive mechanism has little effect on such operations; (2) the lack of an incentive mechanism for third parties results in false investigations by third parties; and (3) rewards from higher levels of government promote strict supervision by local governments, but that the high cost of supervision and rewards for hospitals inhibits the probability of strict supervision. Finally, Matlab 2020a is used for simulation analysis to provide a reference for the government to improve the supervision of healthcare fraud.

Data Management Challenges and Resolutions in the Telecommunication Industry

This paper addresses data management threats, challenges, and measures for mitigation of such data threats in the telecommunication industry. The telecommunications industry handles tonnes of essential, personal and sensitive data on staff, clients and customers. This data is critical in developing product lines and marketing strategies for telecom companies, opening them up to opportunities that otherwise would have been beyond their reach. As a result, telecom companies’ databases are rendered vulnerable to violation and unauthorised access from insiders, third-party entities, as well as hackers. They have become premium target for cyberattacks, malware, interference, data theft and loss of vital data. While systems for the management of data in the telecom industry are crucial in gathering, storing, and analysing data, it is equally important for such systems to be efficient enough to provide a safe and secure mode of accessing, analysing and storing data assets. This is to prevent illegitimate use of sensitive data, cyberattacks, malware and data infringements on telecom data assets.

Two-party interactive secure deduplication with efficient data ownership management in cloud storage

Deduplication, which stores only one copy of duplicate data, is used extensively in cloud storage to reduce the overhead associated with storage. Unfortunately, client-side encryption prevents cloud storage from performing deduplication due to the randomness of traditional encryption. Some existing schemes can balance encryption and deduplication, but their interaction with third-party servers or online users adds additional overhead to the system. Also, the ownership of outsourced data will change frequently due to users’ requests to upload/delete/modify the data. But many existing schemes that can achieve dynamic ownership management have security flaws or require users to manage multiple keys. By focusing on these troubles, we have developed a secure deduplication scheme that does not rely on any third-party entities and supports data ownership management. More specifically, we take advantage of elliptic curve cryptography to design a key-sharing method so that different owners of the same data can share a random key only by interacting with the cloud service provider. And the broadcast encryption is used to manage the ownership of data, and this allows the cloud service provider to control users’ access to outsourced data by updating the broadcast key. In addition, the security analysis shows that the proposed scheme can meet the required security and that it outperforms other related schemes. The detailed simulation comparisons with other related schemes demonstrate that the proposed scheme has good performance.

A Recommender System for Robust Smart Contract Template Classification

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts' correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system's algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts' development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.

Privacy-Preserving and Trustless Verifiable Fairness Audit of Machine Learning Models

In the big data era, machine learning has devel-oped prominently and is widely used in real-world systems. Yet, machine learning raises fairness concerns, which incurs discrimination against groups determined by sensitive attributes such as gender and race. Many researchers have focused on developing fairness audit technique of machine learning model that enable users to protect themselves from discrimination. Existing solutions, however, rely on additional external trust as-sumptions, either on third-party entities or external components, that significantly lower the security. In this study, we propose a trustless verifiable fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. With succinctness and non-interactive of zero knowledge proof, our framework not only guarantees audit integrity, but also clearly enhance security, enabling fair ML models to be publicly auditable and any client to verify audit results without extra trust assumption. Our evaluation on various machine learning models and real-world datasets shows that our framework achieves practical performance.

Risk management for integrated power and natural gas systems against extreme weather: A coalitional insurance contract approach

Integrated power and natural gas systems (IPGSs) are vulnerable to extreme weather. The system operators are facing risks of huge economic losses. An insurance could be the appropriate approach for system operators to manage and transfer the risks of huge economic losses to the third-party entities. However, conventional standalone insurance design will induce the free-ride phenomenon and the missing of integration incentive in IPGSs. Thus, to bridge the gaps, we proposed a novel coalitional insurance design for the IPGSs against the extreme weather. To control the risk of insurer insolvency, the premium of the coalitional insurance is determined based on the resilience assessment-based actuarial framework. To provide appropriate incentive in inter-energy assistance, the indemnity is allocated by a combined damage-based and assistance-based policy. Asymmetric Nash bargaining is adopted to ensure the fair allocation of indemnity. Favorable properties include budget balanced, low safe loading cost, free-ride averse, and integration beneficial are theoretically and numerically proved. Numerical tests are conducted on the modified IEEE 39-bus Belgium 20-node IPGS to validate the effectiveness of the proposed coalitional insurance contract design.

Golden Reference-Free Hardware Trojan Localization Using Graph Convolutional Network

The globalization of the integrated circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging graph convolutional network (GCN). In this work, we convert the circuit design into its intrinsic data structure, graph, and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our approach is automated and does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score, and a false-positive rate below 0.009%.

Guest Editorial: Incubators and Accelerators: Integrating Evolving Incubator Models and Learning from the Past

The papers in this special section focus on the ability to integrate evolving incubator models into the organization. Management and organizational science focusing on the complex process of R&D that leads to the subsequent commercialization has come a long way since the days of post-World War II industrial and research laboratories. In this context, the evolution of the incubator can be retraced from the foundational research in the 1950s and 1960s of Researchon-Research—that explored the R&D process in a number of settings to derive and test theories of organization behavior. Since then, we have seen a concentration of research and practice that have spanned a burgeoning range of incubators models and entities that, in some manner, exist to play a role to support and harness new technology-based start-ups. For more than half a century, these third-party entities in the entrepreneurial ecosystem have spread from the United States to Europe and are a well-established worldwide phenomenon. Business incubators have been largely viewed as an essential means to facilitating the process of knowledge transfer and tech-based commercialization through the provision of office infrastructure, laboratories and equipment, business support services, mentoring, and access to entrepreneurial finance and specialist networks for the founders of these embryonic start-ups.

Cryptanalysis of RSA-Variant Cryptosystem Generated by Potential Rogue CA Methodology

Rogue certificate authorities (RCA) are third-party entities that intentionally produce key pairs that satisfy publicly known security requirements but contain weaknesses only known to the RCA. This work analyses the Murru–Saettone RSA variant scheme that obtains its key pair from a potential RCA methodology. The Murru–Saettone scheme is based on the cubic Pell equation x3+ry3+r2z3−3rxyz=1. The public, e, and private, d key generation process uses the secret parameter ψ=(p2+p+1)(q2+q+1) in place of the standard Euler–phi function ϕ(N)=(p−1)(q−1), where ed≡1(modψ). We prove that, upon obtaining an approximation of ψ, we are able to identify the provided key pair that was maliciously provided even if the private key d size is approximate to ψ. In fact, we are able to factor the modulus N=pq.

Exploring Physician Perspectives on Using Real-world Care Data for the Development of Artificial Intelligence-Based Technologies in Health Care: Qualitative Study.

BackgroundDevelopment of artificial intelligence (AI)–based technologies in health care is proceeding rapidly. The sharing and release of real-world data are key practical issues surrounding the implementation of AI solutions into existing clinical practice. However, data derived from daily patient care are necessary for initial training, and continued data supply is needed for the ongoing training, validation, and improvement of AI-based solutions. Data may need to be shared across multiple institutions and settings for the widespread implementation and high-quality use of these solutions. To date, solutions have not been widely implemented in Germany to meet the challenge of providing a sufficient data volume for the development of AI-based technologies for research and third-party entities. The Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions (pAItient) project aims to meet this challenge by creating a large data pool that feeds on the donation of data derived from daily patient care. Prior to building this data pool, physician perspectives regarding data donation for AI-based solutions should be studied.ObjectiveThis study explores physician perspectives on providing and using real-world care data for the development of AI-based solutions in health care in Germany.MethodsAs a part of the requirements analysis preceding the pAItient project, this qualitative study explored physician perspectives and expectations regarding the use of data derived from daily patient care in AI-based solutions. Semistructured, guide-based, and problem-centered interviews were audiorecorded, deidentified, transcribed verbatim, and analyzed inductively in a thematically structured approach.ResultsInterviews (N=8) with a mean duration of 24 (SD 7.8) minutes were conducted with 6 general practitioners and 2 hospital-based physicians. The mean participant age was 54 (SD 14.1; range 30-74) years, with an average experience as a physician of 25 (SD 13.9; range 1-45) years. Self-rated affinity toward modern information technology varied from very high to low (5-point Likert scale: mean 3.75, SD 1.1). All participants reported they would support the development of AI-based solutions in research contexts by donating deidentified data derived from daily patient care if subsequent data use was made transparent to them and their patients and the benefits for patient care were clear. Contributing to care optimization and efficiency were cited as motivation for potential data donation. Concerns regarding workflow integration (time and effort), appropriate deidentification, and the involvement of third-party entities with economic interests were discussed. The donation of data in reference to psychosomatic treatment needs was viewed critically.ConclusionsThe interviewed physicians reported they would agree to use real-world care data to support the development of AI-based solutions with a clear benefit for daily patient care. Joint ventures with third-party entities were viewed critically and should focus on care optimization and patient benefits rather than financial interests.

Decentralized Web Application for Real Estate Property Transaction using Blockchain and Smart Contract Technology

Real Estate is a wide area consisting of a physical piece of land and the developments on it such as buildings, roads, and other infrastructures. Real Estate can be classified based on the purpose of usage of the property i.e. Residential, Commercial, or Industrial. Since longtime real estate is considered the most secure investment for good returns. But several issues arise in the field of property transactions which are based on the trust that the buyer or seller has in a third-party middleman/broker as well as frauds, inaccurate market statistics, time-consuming processes as well as high processing fees charged by the middlemen/broker. To provide a solution problem a decentralized web application for real estate transactions is proposed in the project. To attain the solution, in this project we propose to design and develop a decentralized web application using blockchain and smart contract technology that avails property buyers and sellers to interact with the help of a private Ethereum network and deploy smart contracts which create a precise and transparent digital property transaction process. This results in the advantages over traditional real estate systems by neglecting the requirement of non-trusted third-party entities, reducing transaction processes as well as transaction costs.

Conceptualizing Intermediary Organizations in Arts Education Through a Cross-Sector Review of the Literature

Research has demonstrated that the successful implementation of arts education necessitates partnerships across sectors, disciplines, and institutions. Many arts education partnerships rely on third-party or intermediary entities to make connections between arts education stakeholders and to help coordinate their partnership initiatives. The advantages of intermediary involvement are promoted by partnership research across domains, but very few studies offer conceptualizations of what an intermediary is and does within arts education. Accordingly, in this article, I (1) review cross-sector examples of intermediary organizations to identify their roles and functions, (2) position the work of intermediaries within arts integration theory and practice, and (3) offer a conceptualization of arts education intermediaries based on the cross-sector review. Implications for arts education policy and practice are also discussed; namely, the usefulness of the conceptual model in identifying intermediary organizations and leveraging their services.