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Data breaches have the potential to weaken employee morale, corporate reputations, and customer and supplier relationships, while also disrupting marketing investments and financial performance. Research on reducing their frequency and harm focuses on tactical solutions, though breaches represent serious, even existential threats to firms. To date, research has not attempted to simultaneously address the closely connected phenomena of preventing and recovering from data breaches. The authors propose that corporate social responsibility (CSR) is a strategic variable offering dual protection: reducing the likelihood of data breaches and attenuating harm when breaches occur. Drawing on stakeholder theory, the authors distinguish between internal (addressing primary stakeholders) and external (addressing secondary stakeholders) CSR. Study 1 shows that external CSR has no prophylactic effect, while moderate and high levels of internal CSR are equally effective at preventing data breaches, compared with low levels of internal CSR. Study 2 assesses mitigation following a data breach by examining (1) short-term effects (in the form of an event study on cumulative abnormal returns) and (2) long-term effects (with time-series analysis of Tobin's q). The results suggest that internal CSR props up financial performance only at high levels while the positive effect of external CSR is short-lived. [ABSTRACT FROM AUTHOR]

In the 21st-century digital age, health data privacy remains a crucial concern. This paper evaluates the effectiveness of the Health Insurance Portability and Accountability Act, known as HIPAA. More specifically, it demonstrates a need for a unified federal framework in the U.S. that aligns with General Data Protection Regulation’s protections to address modern-day cybersecurity threats better. This article argues that in an era of increased globalization, the United States should confront the task of reforming its healthcare data protection law to align with current cybersecurity risks. We begin by examining landmark legislation across American states to reveal inconsistencies between state and federal protective rulings. Later, we uncover the reactive nature of HIPAA, in contrast to GDPR’s proactive and citizen-centric approach. Through evaluating past lawsuits related to patient protection noncompliance, this paper depicts significant differences in the purpose, coverage, and execution of data protection laws between the United States and the European Union. It highlights GDPR’s effectiveness in granting individuals greater control over their data. Furthermore, this article proposes the adoption of newfound systems for standardized risk analysis and enhanced security across healthcare providers.As healthcare becomes more accessible to the American public, the amount of data in this system increases. This nationwide surge in data underscores the critical need to assess whether privacy laws established in the 1990s remain sufficient. Therefore, updates to healthcare legislation are essential to establishing stringent patient protections in response to the significant rise in data breach incidents within the healthcare network.

To secure the privacy, confidentiality, and integrity of Speech Data (SD), the concept of secure Speech Recognition (SR) involves accurately recording and comprehending spoken language while employing diverse security processes. As the Internet of Things (IoT) rapidly evolves, the integration of SR capabilities into IoT devices gains significance. However, ensuring the security and privacy of private SD post-integration remains a critical concern. Despite the potential benefits, implementing the proposed Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR and integrating it with IoT devices may encounter complexities due to diverse device types. Moreover, the challenge of maintaining data security and privacy for assigned SD in practical IoT settings poses a significant hurdle. Ensuring seamless interoperability and robust security measures is essential. We introduce the Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR, utilizing retrieved aspects as speech data. Gathering a diverse range of SD from speakers with varying linguistic backgrounds enhances the accuracy of the SR system. Preprocessing involves Z-score normalization for robustness and mitigation of outlier effects. The Perceptual Linear Prediction (PLP) technique facilitates efficient extraction of essential acoustic data from speech sources. Addressing data security, Elliptic Curve Cryptography (ECC) is employed for encryption, particularly suited for resource-constrained scenarios. Our study evaluates the SR system, employing key performance metrics including accuracy, precision, recall, and F1 score. The thorough assessment demonstrates the system's remarkable performance, achieving an impressive accuracy of 96%. The primary objective revolves around appraising the system's capacity and dependability in accurately transcribing speech signals. By proposing a comprehensive approach that combines the RSO-HMM for SR, data preprocessing techniques, and ECC encryption, this study advocates for the wider adoption of SR technology within the IoT ecosystem. By tackling critical data security concerns, this approach paves the way for a safer and more efficient globally interconnected society, encouraging the broader utilization of SR technology in various applications. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is designed to protect privacy of participants by not allowing direct access to the participants' local datasets and training processes. This limitation hinders the server's ability to verify the authenticity of the model updates sent by participants, making FL vulnerable to poisoning attacks. In addition, gradients in FL process can reveal private information about the local dataset of the participants. However, there is a contradiction between improving robustness against poisoning attacks and preserving privacy of participants. Privacy-preserving techniques aim to make their data indistinguishable from each other, which hinders the detection of abnormal data based on similarity. It is challenging to enhance both aspects simultaneously. The growing concern for data security and privacy protection has inspired us to undertake this research and compile this survey. In this survey, we investigate existing privacy-preserving defense strategies against poisoning attacks in FL. First, we introduce two important classifications of poisoning attacks: data poisoning attack and model poisoning attack. Second, we study plaintext-based defense strategies and classify them into two categories: poisoning tolerance and poisoning detection. Third, we investigate how the combination of privacy techniques and traditional detection strategies can be achieved to defend against poisoning attacks while protecting the privacy of the participants. Finally, we also discuss the challenges faced in the area of security and privacy. [ABSTRACT FROM AUTHOR]

The EU's Digital Markets Act will require certain large tech firms' ecosystems to become more open. The Act contains few exceptions – but law-makers did include special protections for undertakings providing operating systems, like Apple and Google, to protect security. This paper explores how the Commission should assess these measures. It argues that security is not a "trump card" to undermine the Act's objectives – instead, Apple and Google must balance security benefits of their measures against any limitations on contestability. Furthermore, the Act should continue to allow both firms to differentiate their products' approach to security. However, Apple's (and to a lesser extent Google's) security measures are likely to be heavily disputed. The Act does not give the Commission effective powers to efficiently resolve such disputes nor to force gatekeepers to change their approaches to security. [ABSTRACT FROM AUTHOR]

Financial institutions turn to cloud services and entrust cloud providers with increasingly important tasks. The relationship between these two groups has largely existed outside regulatory parameters, but Digital Operational Resilience Act (DORA) is set to change that dynamic when it becomes applicable in 2025. This paper tackles two issues in this context. Firstly, it identifies the legal and practical challenges that EU financial institutions may encounter while adopting and utilising cloud services under the DORA regime. Challenges during the adoption stage include the selection of providers, decisions regarding the transfer of critical or important functions, and the choice of contractual clauses. In the usage stage, data security and financial stability emerge as the most critical concerns. One overarching difficulty is concentration risk. Secondly, the question of how to address these issues arises. Fortunately, identified challenges can be mitigated if financial institutions choose appropriate cloud deployment and service models. [ABSTRACT FROM AUTHOR]

Developing a secured and accurate disease diagnosis framework in the healthcare cloud systems are still remains one of the crucial problems in recent times. Due to the rapid growth of information and technology, it is highly essential to protect the patient health information against the unauthenticated users for ensuring the privacy and security. For this purpose, the different types of security approaches are developed in the conventional works, which are mainly focused on increasing the privacy of medical data stored in the cloud systems. However, it lacks with the major issues of increased computational overhead, communication cost, lack of security, complex mathematical modeling, and increased time consumption. Therefore, the proposed work objects to implement an intelligent and advanced privacy preserving framework, named as, lightweight signing signature based secured jellyfish data aggregation (LS3JDA) for ensuring the privacy of medical data in the healthcare cloud systems. The main contribution of this research work is to develop a new and lightweight privacy preservation model by incorporating the functions of both AI and signing signature algorithms for assuring data security in cloud systems. Moreover, it simplified the process of entire privacy preservation system with low computational burden and high data security. It also objects to accurately predict the type of disease based on the patients' medical history by using an advanced random forest (RF) machine learning methodology. The novel contributions of this work are, a message signing signature generation algorithm is used to strengthen the security of patients' medical data, and a jelly fish optimization (JFO) methodology is used to improve the process of data aggregation. The primary advantages of the proposed system are reduced processing time, low computational burden, and simple to deploy. For validating the results of the proposed model, several parameters include level of security, time, throughput, latency, signature cost, and communication overhead are assessed during evaluation. Moreover, the results are contrasted with some of the recent privacy preservation models for assuring the superiority of the proposed framework. Here, the overall processing time is reduced up to 1.5 ms, and communication overhead is reduced up to 100 bytes with the use of optimization integrated data aggregation model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) nodes consist of sensors that collect environmental data and then perform data exchange with surrounding nodes and gateways. Cybersecurity attacks pose a threat to the data security that is being transmitted in any IoT network. Cryptographic primitives are widely adopted to address these threats; however, the substantial computation demands limit their applicability in the IoT ecosystem. In addition, each IoT node varies with respect to the area and throughput (TP) requirements, thus demanding flexible implementation for encryption/decryption processes. To solve these issues, this work implements the NIST lightweight cryptography standard, Ascon, on a SAED 32 nm process design kit (PDK) library by employing loop folded, loop unrolled and fully unrolled architectures. The fully unrolled architecture can achieve the highest TP but at the cost of higher area utilisation. Unrolling by a lower factor results in lower area implementations, enabling the exploration of design space to tackle the trade-off between area and TP performance of the design. The implementation results show that, for loop folded architecture, Ascon-128 and Ascon-128a require 36.7k μ m 2 and 38.5k μ m 2 chip area, respectively compared to 277.1k μ m 2 and 306.6k μ m 2 required by their fully unrolled implementations. The proposed implementation strategies can adjust the number of rounds to accommodate the varied requirements of IoT ecosystems. An implementation with an open-source 45 nm PDK library is also undertaken for enhanced generalization and reproducibility of the results. [ABSTRACT FROM AUTHOR]

Cloud computing is widely used for medical data sharing due to its convenience and scalability in data access. In practice, patients prefer to share their medical data with healthcare professionals in a controlled manner due to the sensitive information it implies. Specifically, patients often delegate data ownership to healthcare professionals for more comprehensive treatment. When patients are cured, they wish to revoke the delegation of data ownership. In this process, data security must be ensured when implementing ownership delegation revocation. However, although schemes have been proposed to support fine-grained access control, existing data-sharing systems do not meet the need for dynamic management of data ownership between doctors and patients, and the security issues posed by revocation of ownership delegation remain unexplored. In response to this problem, we propose a time-limited data ownership delegate system (DLDS system) with revocation security, which enables data owners to flexibly control data sharing according to time and user requirements. We design a time-based prefix tree that can revoke data ownership without compromising patient data privacy. To verify the validity of data ownership delegation in a privacy-preserving manner, we design a new interactive authentication method using the Paillier encryption method and inner product computation to ensure that only authorized users can access the data. Finally, the proposed scenarios were thoroughly discussed and simulated experimentally to assess their safety and effectiveness. [ABSTRACT FROM AUTHOR]

Fog computing is a revolutionary technology that, by expanding the cloud computing paradigm to the network edge, brings a significant achievement in the resource-constrained IoT applications in intelligent environments. However, security matters still challenge the extensive deployment of fog computing infrastructure. Ciphertext policy attribute-based encryption prepares a solution for data sharing and security preservation issues in fog-enhanced intelligent environments. Nevertheless, the lack of an effective mechanism to moderate the execution time of CP-ABE schemes due to the diversity of attributes used in secret key and access structure, as well as ensuring data security, practically restricts the deployment of such schemes. In this regard, a collaborative semantic model, including an outsourced CP-ABE scheme with the attribute revocation ability, together with an impressive AES algorithm relying on an ensemble learning system, was proposed in this study. The ensemble learning model uses multiple classifiers, including the GMDH, SVM, and KNN, to specify attributes corresponding to CP-ABE. The Dragonfly algorithm with a semantic leveling method generates outstanding and practical feature subsets. The experimental results on five smart building datasets indicate that the recommended model performs more accurately than existing methods. Also, the encryption, decryption, and attribute revocation execution time are significantly modified with the average time of 1.95, 2.11, and 14.64 ms, respectively, compared to existing works and conducted the scheme's security. [ABSTRACT FROM AUTHOR]

Copyright of Ethik in der Medizin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital preservation encompasses policies, technologies, and strategies to ensure that digital objects and collections will sustain for future use. This study aimed at investigating the trends toward digital preservation in terms of in-house activities versus outsourcing by systematically reviewing the extant literature. The relevant studies were identified by accessing multiple databases and indexing and abstracting services. After retrieving the relevant literature, necessary filtering was done by following the PRISMA protocols, and thereafter the selected studies were finalized for further review. The meta-analysis of the final studies affirms a strong global preference of libraries, archives, and other cultural and memory organizations toward in-house activities for the preservation of their digital objects and collections compared to outsourcing digital preservation activities by third parties. The results of this study provide a holistic view of how digital assets are being preserved in memory institutions across the world and underpin the global management (in-house or outsourced) approaches for digital preservation. [ABSTRACT FROM AUTHOR]

This research paper conducts a systematic review of the integration of blockchain technology with Enterprise Resource Planning (ERP) systems, a burgeoning field at the intersection of advanced technology and business management. The primary objective is to bridge the knowledge gap by synthesizing existing literature, providing insights into the current state, challenges, and potential of blockchain in enhancing ERP systems. The study follows a structured methodology involving four key stages: Literature Retrieval, Literature Screening, Biblio-metric Analysis, and Content Analysis. A comprehensive literature search in the Scopus database resulted in the identification and analysis of 250 scholarly articles, narrowed down to 66 relevant publications. The findings highlight a significant trend towards decentralized ERP solutions, driven by blockchain's promise of enhanced security, transparency, and efficiency. Key areas impacted by blockchain integration include supply chain management, accounting, security, traceability, purchasing, and process automation within ERP systems. The bibliometric analysis reveals a growing academic interest in this domain, with a focus on scalability, data security, and interoperability. However, the integration faces challenges such as technical complexity, cultural resistance, and regulatory uncertainties. The study concludes that while blockchain presents transformative opportunities for ERP systems, realizing this potential requires overcoming substantial technical and organizational hurdles. Future research should concentrate on developing scalable, interoperable blockchain solutions, tailored to specific industry needs and compliant with regulatory standards, thereby facilitating widespread adoption and maximizing the benefits of blockchain in ERP systems. [ABSTRACT FROM AUTHOR]

With the rapid growth of big data, extracting meaningful knowledge from data is crucial for machine learning. The existing Swarm Learning data collaboration models face challenges such as data security, model security, high communication overhead, and model performance optimization. To address this, we propose the Swarm Mutual Learning (SML). Firstly, we introduce an Adaptive Mutual Distillation Algorithm that dynamically controls the learning intensity based on distillation weights and strength, enhancing the efficiency of knowledge extraction and transfer during mutual distillation. Secondly, we design a Global Parameter Aggregation Algorithm based on homomorphic encryption, coupled with a Dynamic Gradient Decomposition Algorithm using singular value decomposition. This allows the model to aggregate parameters in ciphertext, significantly reducing communication overhead during uploads and downloads. Finally, we validate the proposed methods on real datasets, demonstrating their effectiveness and efficiency in model updates. On the MNIST dataset and CIFAR-10 dataset, the local model accuracies reached 95.02% and 55.26%, respectively, surpassing those of the comparative models. Furthermore, while ensuring the security of the aggregation process, we significantly reduced the communication overhead for uploading and downloading. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: In recent years, the robotic process automation (RPA) technology, a software-based method to automate routine tasks in business processes, has gained significant interest and adoption. However, many implementation projects fail and current literature lacks a synthesis and comprehensive overview of factors that challenge the implementation of RPA, have an impact on success or failure of projects, or, play an enabling role in an RPA project. Hence, the purpose of this research is to identify key factors that should be considered by organizations when conducting an RPA project. Design/methodology/approach: The paper adopts a qualitative methodology based on data collected in a systematic literature review (SLR) and interviews with 10 RPA experts. Using inductive coding, an integrated framework of key factors is developed. Findings: The results suggest that the key factors for a successful RPA introduction can be divided into human, organizational and technical factors. Important aspects include for example project management techniques, capabilities and skills of employees, as well as data security considerations. Originality/value: The paper contributes to knowledge by synthesizing previously dispersed knowledge into an integrated framework, as well as by complementing previous results with new qualitative, empirical data. Additionally, the RPA-specific factors are put into the perspective of persistent problems in information systems development. [ABSTRACT FROM AUTHOR]

In today's digital world, where the amount of information that is stored and processed in cyberspace is constantly growing, the issue of personal data protection is becoming more and more relevant. Personal data, such as financial information, medical records, and social media data, is a valuable resource that can be used for both legitimate and malicious purposes. Despite the continuous improvement of security technologies, the growing complexity of cyber threats and the evolution of cyber attack methods create new challenges for the protection of personal information. In this connection, there is a need to analyze the modern problems of personal data protection, in particular the legal, technical and ethical aspects of this issue. Ensuring the security of personal data remains an urgent problem today, because more and more people use it. On the Internet, they pass authorization on various resources, using their personal data for this purpose, and more and more different institutions create peculiar databases, not always taking care of their safety. The insufficient measure of ensuring the security of personal data in cyberspace leads to excessive activity of cyberattacks and the commission of various manipulations with personal data. The relevance of the topic of personal data protection in cyberspace is due to several important factors that have a significant impact on modern society, economy and security. The main aspects that emphasize the relevance of this issue: the growth of digitalization; cyber threats; legal requirements; social responsibility and trust; economic consequences; technological challenges; international relations and geopolitics. Thus, the relevance of the topic of personal data protection in cyberspace is due to the complexity and global nature of this issue, as well as its importance for preserving privacy, security and trust in the digital world. This article is aimed at researching key challenges in the field of personal data protection in cyberspace, as well as providing recommendations for increasing the level of security and minimizing the risks of information leakage. [ABSTRACT FROM AUTHOR]

Summary: Blockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future. [ABSTRACT FROM AUTHOR]

IoT-based healthcare (HC) systems face security and efficiency challenges. Existing solutions, such as secure transmission models, enhanced security protocols, and secure frameworks, neglect patient authentication and rely on resource-intensive cryptography, leading to vulnerabilities and increased energy consumption. The use of Exponential Key-based Elliptical Curve Cryptography (EKECC) in previous work raises concerns about its long-term viability against quantum computing threats. Additionally, the Path-Weighted Q Reinforcement Learning (PWQRL) technique is limited to discrete action spaces, hindering its applicability in IoT-based HC systems with continuous action spaces. To address these issues, Enhanced Mayfly with Active elite approach Clustering based Deep Q Learner Routing with Enhanced Binary ring-learning-with-errors (EMACDQLEB) protocol is proposed in this paper. EMACDQLEB incorporates a quantum-resistant cryptographic scheme based on Enhanced Binary Ring-Learning-with-Errors (EBRLWE) and a routing algorithm using Deep Q-Networks (DQN). EBRLWE employs an additional encryption key to enhance data security against quantum threats. DQN enables optimal path selection for data transmission by using a Deep Neural Network (DNN) to approximate the Q-value function, improving routing efficiency. Experimental results show that EMACDQLEB outperforms previous methods in average energy consumption, reliability, and communication overhead. This paper aims to mitigate vulnerabilities and improve the HC infrastructure in the IoT era. [ABSTRACT FROM AUTHOR]

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. [ABSTRACT FROM AUTHOR]

Objectives: World Health Organization issued Joint Statement on Data Protection and Privacy in the COVID-19 Response stating that collection of vast amounts of personal data may potentially lead to the infringement of fundamental human rights and freedoms. The Organization for Economic Cooperation and Development called on national governments to adhere to the international principles for data security and confidentiality. This paper describes the methods used to assist the Ministry of Health in bringing awareness of the data ownership, confidentiality and security principles to COVID-19 responders. Methods: The Sierra Leone Epidemiological Data (SLED) Team data managers conducted training for groups of COVID-19 responders. Training included presentations on data confidentiality, information disclosure, physical and electronic data security, and cyber-security; and interactive discussion of real-life scenarios. A game of Jeopardy was created to test the participant's knowledge. Results: This paper describes the methods used by the SLED Team to bring awareness of the DOCS principles to more than 2,500 COVID-19 responders. Conclusion: Similar efforts may benefit other countries where the knowledge, resources, and governing rules for protection of personal data are limited. [ABSTRACT FROM AUTHOR]

Pattern recognition of optical label is utilized not only in packet switches but also in the field of data security due to its wide range of applications. In particular, pattern recognition using waveguide devices is highly valued because it eliminates the need for encoder processing. However, existing methods suffer from the problem of degraded contrast ratio because half of the input power are emitted from non‐target waveguides. In this article, a method is proposed to improve the contrast ratio employing a 4×$\times$4 multi‐mode interference (MMI) coupler with multiple input signals. The theoretical results reveal that the proposed method improves the contrast ratio from 3.0 dB to 9.5 dB and ∞$\infty$ dB for the three‐input and four‐input cases, respectively, for quadrature phase‐shift keying (QPSK)‐modulated signal. Furthermore, numerical simulation was performed through the three dimension finite‐difference time‐domain (3D‐FDTD) method, and the proposed scheme successfully discriminated each QPSK pattern. The power value |E|2$|E|^2$ at all ports closely matches the theoretical value, and the ratios of 9.5 dB and >$>$30.0 dB are numerically obtained. [ABSTRACT FROM AUTHOR]

The rapid growth of Information and communication technology not only has positive impacts but also unveils opportunities for data and information security threats. In recent years, many researchers have worked on developing methods to enhance data security, particularly through data hiding techniques aimed at safeguarding communications by concealing their existence. With the same objective in mind, this study introduces a novel method for Reversible Data Hiding (RDH) based on a combination of difference expansion (DE) and a modulus function. Our method enables the embedding of 3-bit data into 2-bit Least Significant Bit (LSB) difference values of pixel pairs formed in rectangular blocks. Based on the experimental results, the payload capacity of our method can reach 0.3953 bpp with a PSNR of 53.5900 dB on common images and 0.5764 bpp with a PSNR of 52.9234 dB on medical images. Our method consistently achieves high payload capacity with good visual quality, and our method surpasses previous approaches in terms of performance, payload capacity, and visual quality. [ABSTRACT FROM AUTHOR]

IoT devices generate a massive amount of sensitive data, which is transferred tremendously to the cloud for processing and decision-making. The most significant issues that need to be solved for IoT devices are improving energy efficiency and guaranteeing data security while several devices are connected. In this paper, for edge computing, a hybrid algorithm is proposed that uses compression and encryption in the same manner to improve efficiency in terms of energy and data security in IoT devices. The authenticated encryption with associated data (AEAD) ChaCha12-Poly1305 algorithm and improved SZ 2.1 compression are used in this hybrid architecture. While sending the data to the edge, confidentiality, integrity, and authentication are maintained. Several experiments were conducted considering the driver stress detection dataset (Bernstein DJ. Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 4986:84–97 (2008); Lakshminarasimhan S et al. LNCS 6852(1):366–379 (2011)) and gas-sensor dataset (Ibarria L et al. Comput Graph Forum 22(3):343–348 (2003)) for monitoring the home activity to validate this approach. The performance of the proposed improved SZ 2.1 compression algorithm is compared with the five baseline algorithms including SZ 1.4, original SZ 2.1, selective compression, algorithm-based fault tolerance (ABFT), and digit rounding algorithms. The key parameters used in the experiment to measure the performance of the proposed algorithm are data reduction, compression ratio, power consumption, encryption time, total processing time, and error. Using the improved SZ 2.1 compression algorithm in conjunction with the ChaCha12-Poly1305 AEAD algorithm, the device's battery life is also enhanced by about 10% while ensuring the security of data disseminated to the Edge. The use of the proposed secure hybrid model reduces both encryption and overall processing time by 95% and 98% respectively for both datasets. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions Compared to usual care, specialty services for first‐episode psychosis (FES) have superior patient outcomes. The Early Psychosis Intervention Network (EPINET), comprised of eight U.S. regional clinical networks, aims to advance the quality of FES care within the ethos of learning healthcare systems (LHS). Among these, the Academic Community (AC) EPINET was established to provide FES care, collect common data elements, leverage informatics, foster a culture of continuous learning and quality improvement, and engage in practice‐based research.We designed and implemented a novel LHS of university‐affiliated FES programs within a hub (academic leadership team) and spoke (FES clinics) model. A series of site implementation meetings engaged stakeholders, setting the stage for a culture that values data collection and shared learning. We built clinical workflows to collect common data elements at enrollment and at consecutive 6‐month intervals in parallel to an informatics workflow to deliver outcome visualizations and drive quality improvement efforts.All six clinical sites successfully implemented data capture workflows and engaged in the process of designing the informatics platform. Upon developing the structure, processes, and initial culture of the LHS, a total of 614 patients enrolled in AC‐EPINET, with the most common primary diagnoses of schizophrenia (32.1%) and unspecified psychotic disorders (23.6%). Visualized outcomes were delivered to clinical teams who began to consider locally relevant quality improvement projects.AC‐EPINET is a novel LHS, with a simultaneous focus on science, informatics, incentives, and culture. The work of developing AC‐EPINET thus far has highlighted the need for future LHS’ to be mindful of the complexities of data security issues, develop more automated informatic workflows, resource quality assurance efforts, and attend to building the cultural infrastructure with the input of all stakeholders. [ABSTRACT FROM AUTHOR]

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF's potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security. [ABSTRACT FROM AUTHOR]

The consequences of an aging society pose one of the major challenges of our time. Technology solutions that alleviate the strain on the overburdened healthcare system can effectively support individuals needing assistance. Still, technology acceptance is a crucial variable in understanding what drives users to utilize such applications. This study examined the role of trust and privacy in the accepted use of ambient technologies for health-related purposes. Using an online survey we collected data from N = 102 respondents. Findings revealed that participants are willing to use ambient technology for health. Benefits and barriers are traded off, whereby the advantages outweigh the perceived barriers. Trust conditions to be fulfilled include data security, technical soundness, and health-related aspects. Having control over personal information results as the most important privacy aspect, followed by the supervision of social contacts. Regression analyses revealed perceived motives and barriers, technical soundness, and health-related trust to be significant predictors of acceptance. [ABSTRACT FROM AUTHOR]

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations. [ABSTRACT FROM AUTHOR]

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN. [ABSTRACT FROM AUTHOR]

Background: The impetus for policies promoting medical data sharing in China has gained significant traction. Nonetheless, the present legal and ethical framework governing the research use of medical data in China, is characterized by a more restrictive rather than permissive approach. The proportion of Chinese medical data being leveraged for scientific research still has room for improvement at present, indicating a significant untapped potential for advancing medical knowledge and improving healthcare outcomes. Building upon this research, we aim to delve deeper into the challenges researchers encounter in the sharing of medical data through focus group interviews. Methods: We conducted two focus group interviews study with researchers representing diverse disciplines to explore their perspectives on 21 June 2021 and 28 July 2021. A total of seventeen researchers willingly participated in this study, representing various professional backgrounds. Similar codes were merged. Research team discussions were also utilized to select interviewees' statements that were regarded as typical or representative. Results: The respondents demonstrated a strong understanding that medical data should not be disseminated arbitrarily, recognizing the importance of sharing data in compliance with laws. Through the interview, we found that although respondents stressed the importance of careful consideration regarding if and when this information can be responsibly released, none of the respondents raised the issue of necessitating consent from data subjects for the research use of medical data. This observation sharply contrasts with the stringent separate consent provisions for secondary data use outlined in the PIPL. Conclusions: The findings from the focus group studies shed light on researchers' barriers and ethical challenges towards medical data sharing for scientific research, highlighting their deep concern for data security and cautious approach to sharing. The key objectives aimed at facilitating and enabling the reuse of medical data encompass enhancing interoperability, harmonizing data standards, improving data quality, safeguarding privacy, ensuring informed consent, incentivizing patients, and establishing explicit regulations pertaining to data access and utilization. [ABSTRACT FROM AUTHOR]

In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection. [ABSTRACT FROM AUTHOR]