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The Internet of Medical Things, Smart Devices, Information Systems, and Cloud Services have led to a digital transformation of the healthcare industry. Digital healthcare services have paved the way for easier and more accessible treatment, thus making our lives far more comfortable. However, the present day healthcare industry has also become the main victim of external as well as internal attacks. Data breaches are not just a concern and complication for security experts; they also affect clients, stakeholders, organizations, and businesses. Though the data breaches are of different types, their impact is almost always the same. This study provides insights into the various categories of data breaches faced by different organizations. The main objective is to do an in-depth analysis of healthcare data breaches and draw inferences from them, thereby using the findings to improve healthcare data confidentiality. The study found that hacking/IT incidents are the most prevalent forms of attack behind healthcare data breaches, followed by unauthorized internal disclosures. The frequency of healthcare data breaches, magnitude of exposed records, and financial losses due to breached records are increasing rapidly. Data from the healthcare industry is regarded as being highly valuable. This has become a major lure for the misappropriation and pilferage of healthcare data. Addressing this anomaly, the present study employs the simple moving average method and the simple exponential soothing method of time series analysis to examine the trend of healthcare data breaches and their cost. Of the two methods, the simple moving average method provided more reliable forecasting results.

Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. By developing measures for data quality, this article suggests a framework to address such a question: “Which one should I trust more: a 1% survey with 60% response rate or a self-reported administrative dataset covering 80% of the population?” A 5-element Euler-formula-like identity shows that for any dataset of size n, probabilistic or not, the difference between the sample average X̅n and the population average X̅N is the product of three terms: (1) a data quality measure, ρR, X, the correlation between Xj and the response/recording indicator Rj ; (2) a data quantity measure, ( N − n ) / n , where N is the population size; and (3) a problem difficulty measure, σX , the standard deviation of X. This decomposition provides multiple insights: (I) Probabilistic sampling ensures high data quality by controlling ρR, X at the level of N −1/2; (II) When we lose this control, the impact of N is no longer canceled by ρR, X , leading to a Law of Large Populations (LLP), that is, our estimation error, relative to the benchmarking rate 1/√n, increases with √N; and (III) the “bigness” of such Big Data (for population inferences) should be measured by the relative size f = n/N, not the absolute size n; (IV) When combining data sources for population inferences, those relatively tiny but higher quality ones should be given far more weights than suggested by their sizes. Estimates obtained from the Cooperative Congressional Election Study (CCES) of the 2016 US presidential election suggest a ρR, X ≈ −0.005 for self-reporting to vote for Donald Trump. Because of LLP, this seemingly minuscule data defect correlation implies that the simple sample proportion of the self-reported voting preference for Trump from 1% of the US eligible voters, that is, n ≈ 2,300,000, has the same mean squared error as the corresponding sample proportion from a genuine simple random sample of size n ≈ 400, a 99.98% reduction of sample size (and hence our confidence). The CCES data demonstrate LLP vividly: on average, the larger the state’s voter populations, the further away the actual Trump vote shares from the usual 95% confidence intervals based on the sample proportions. This should remind us that, without taking data quality into account, population inferences with Big Data are subject to a Big Data Paradox: the more the data, the surer we fool ourselves.

Recent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastroenteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, digital pathology is not yet accessible owing to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission, and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We designed and prototyped a digital pathology system that uses blockchain-based smart contracts using the nonfungible token (NFT) standard and the Interplanetary File System for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis. The main objectives of this study are to highlight the issues in digital pathology and suggest that a software architecture-based blockchain and the Interplanetary File System create a low-cost data storage and transmission technology. We used the design science research method consisting of 6 stages to inform our design overall. We innovated over existing public-private designs for blockchains but using a 2-layered approach that separates actual file storage from metadata and data persistence. Here, we identified key challenges to adopting digital pathology, including challenges concerning long-term storage and the transmission of information. Next, using accepted frameworks in NFT-based intelligent contracts and recent innovations in distributed secure storage, we proposed a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js helped us address several challenges facing digital pathology. We demonstrated how our solution, which combines NFT smart contract standard with persistent decentralized file storage, solves most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis. We identified technical limitations that increase costs and reduce the mass adoption of digital pathology. We presented several design innovations using NFT decentralized storage standards to prototype a system. We also presented the implementation details of a unique security architecture for a digital pathology system. We illustrated how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrated how improving these factors sets the stage for improving data quality and standardized application of machine learning and artificial intelligence to such data.

This study proposes a Blockchain-based Student Identity Management System (BSIMS) to address critical challenges in current identity management systems in higher education, such as trust deficits, data confidentiality breaches, and lack of interoperability. BSIMS is designed to enhance the personal learning environment (PLE) in higher education by providing secure, reliable, transparent, and efficient identity verification and data management services. The model was developed based on insights from semi-structured interviews with seven ICT experts from both academia and industry, focusing on personal data confidentiality within the PLE. Thematic analysis identified key challenges, including data security, control, circulation, trust, user needs and experience, and system performance and scalability. BSIMS comprises modules such as PLE participants, platform, identity authentication, blockchain verification, shared learning content, blockchain technology, and a Learning Record Store (LRS) verification system. These modules are characterized by autonomy, security, flexibility, and efficiency. The study details the functionality and interactions of these components while acknowledging the model’s limitations and suggesting future research directions. This research highlights the potential of blockchain technology to create an autonomous, secure, and efficient identity management mechanism within higher education's PLE. The innovation lies in applying blockchain to the PLE's identity verification process and introducing a feature to record user behavior using blockchain, thereby enhancing personal data confidentiality and demonstrating blockchain’s potential in education.

Currently huge amount of data used to stored, extracted and transacted via various stand alone and internet based applications. These applications are extended towards the verticals like huge databases, data warehouses, cloud computing services and various client-server applications. In all these applications important data used to float day in day out. Therefore preserving user authentication & access control is extremely important aspect of information security. Here an attempt is made to generate an implicit authentication system using multi-share visual key cryptography which will generate strong password keys by using images. Initially various images will be fused to form a resultant image than on this fused visual key cryptography will be performed which will provide multiple shares, out of these one of the share is selected to generate strong password strings/keys to accomplish the task of access control or user authentications.

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

Cybersecurity threats pose a significant risk to IoT-based smart healthcare technologies by compromising patient safety, disrupting services, and exposing sensitive health data to unauthorized access and misuse. This research aims to strengthen data integrity and confidentiality in smart healthcare systems by developing edge-level cryptographic strategies tailored for IoT-enabled edge environments, addressing the security and privacy challenges of resource-constrained devices. The proposed methodology Cryptographic Security Framework with SignaVault Authentication (CSFVA) integrates lightweight cryptographic techniques with edge computing to secure healthcare data efficiently and in real time. The novelty of this research lies in the unified implementation of a layered cryptographic pipeline, comprising Elliptic Curve Cryptography (ECC) for encryption, a Secure Hash Crypto Technique (SHCT) for data integrity, and a Signa-Vault (SV) authentication mechanism for user and device verification. This tri-layered approach ensures data confidentiality, integrity, and authenticity while sup porting the low-latency requirements of edge computing environments. Performance evaluation shows the model's efficiency, achieving a processing time of 5.81 seconds, memory use of 45.78 MB, power consumption of 4.2 W, and throughput of 99.67%. These results indicate that the proposed solution effectively balances security and resource efficiency, making it suitable for resource-limited IoT healthcare and scalable smart healthcare systems.

Since the early 2000s, the proliferation of cameras in devices such as mobile phones, closed-circuit television (CCTV), or body cameras has led to a sharp increase in video recordings of human interaction and behavior. Through websites that employ user-generated content (e.g., YouTube) and live streaming sites (e.g., GeoCam), access to such videos virtually is at the fingertips of social science researchers. Online video data offer great potential for social science research to study an array of human interaction and behavior, but they also raise ethical questions to which existing guidelines and publications only provide partial answers. In our article we address this gap, drawing on existing ethical discussions and applying them to the use of online video data. We examine five areas in which online video research raises specific questions or promises unique potentials: informed consent, analytic opportunities, privacy, transparency, and minimizing harm to participants. We discuss their interplay and how these areas can inform practitioners, reviewers, and interested readers of online video studies when evaluating the ethical standing of a study. With this study, we contribute to an informed and transparent discussion about ethics in online video research.

WhatsApp is a popular messaging application frequently used by physicians and healthcare organizations that can improve the continuity of care and facilitate effective health services provision, especially in acute settings. However WhatsApp does not comply with the rules of the European GDPR and the US HIPA Act. So it is inappropriate to share clinical information via WhatsApp.For this reason alternatives to Whatsapp are considered. In particular, the features that must have secure messaging apps to be in compliance with GDPR and HIPAA and to protect patient data will be discussed. The aim is to encourage healthcare organizations and physicians to abandon WhatsApp and to adopt one of the many secure messaging apps now available, some of them at no cost.