Data Privacy Research Articles

Federated Learning's Dynamic Defense Against Byzantine Attacks: Integrating SIFT-Wavelet and Differential Privacy for Byzantine Grade Levels Detection

Federated learning, which enables decentralized training across multiple devices while maintaining data privacy, is susceptible to Byzantine poisoning attacks. This paradigm reduces the need for centralized data storage and transmission, thereby mitigating privacy risks associated with traditional data aggregation methods. However, FL introduces new challenges, notably susceptibility to Byzantine poisoning attacks, where rogue participants can tamper with model updates, threatening the consistency and security of the aggregated model. Our approach addresses this vulnerability by implementing robust aggregation methods, sophisticated pre-processing techniques, and a novel Byzantine grade-level detection mechanism. We introduce a federated aggregation operator designed to mitigate the impact of malicious clients. Our pre-processing includes data loading and transformation, data augmentation, and feature extraction using SIFT and wavelet transforms. Additionally, we employ differential privacy and model compression to improve the robustness and performance of the federated learning framework. Our approach is assessed using a tailored neural network model applied to the MNIST dataset, achieving 97% accuracy in detecting Byzantine attacks. Our results demonstrate that robust aggregation significantly improves the resilience and performance. This comprehensive approach ensures the integrity of the federated learning process, effectively filtering out adversarial influences and sustaining high accuracy even when faced with adversarial Byzantine clients.

The evolution of marketing research in the context of digital transformation of society

Marketing research’s relevance in digital transformation is paramount for ensuring business process efficiency. The article aims to analyse the impact of new digital tools on marketing research, identify the main advantages of these methods, and develop recommendations for their optimisation. The methodology involves a comprehensive approach, which includes analysing the experience of international companies, assessing the market, and the practice of applying the latest technologies in marketing research. The results revealed that using big data and social media analytics increases the accuracy and efficiency of marketing campaigns. The article highlights the need to adapt digital approaches to market specifics and consumer needs. The study results indicate that the revenues of the marketing research industry significantly influenced global GDP from 2009 to 2022, with the most substantial impact observed in North America and the weakest in Latin America. The practical significance lies in integrating modern technologies into the marketing research process, which requires marketers to have high technological proficiency. The article outlines the role of data privacy, which demands a responsible approach to information from companies. Several recommended measures are provided to improve marketing research, implement personalised strategies, and enhance staff qualifications. The prospects for further research include developing comprehensive programs that enable the effective use of digital technologies in marketing research.

Analyzing financial analysts' role in business optimization and advanced data analytics

This paper explores the evolving role of financial analysts in business optimization, focusing on their increasing reliance on advanced data analytics to drive strategic decision-making. It examines the historical shift from traditional financial analysis to more data-centric responsibilities, highlighting how modern financial analysts contribute to operational efficiency, cost reduction, and revenue growth through data-driven insights. Integrating advanced tools such as artificial intelligence, machine learning, and predictive analytics has enabled financial analysts to interpret complex datasets, optimize business processes, and improve market positioning. However, adopting these technologies comes with challenges, including a steep learning curve, data integration difficulties, and ethical concerns related to data privacy and bias in AI algorithms. This paper also discusses future trends in financial analysis, emphasizing the growing importance of real-time data analysis and automation in business optimization. The paper concludes by stressing the need for financial analysts to continually adapt to the rapidly evolving technological landscape to effectively shape corporate strategy.

A Blockchain and Zero Knowledge Proof Based Data Security Transaction Method in Distributed Computing

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Exploring the Role of AI in Automating Content Marketing: Unlocking Opportunities and Navigating Challenges

As artificial intelligence (AI) continues to evolve, it has significantly influenced the field of content marketing, offering new opportunities for automation. This research explores how AI is transforming content marketing by automating processes such as content creation, curation, personalization, and distribution. AI-driven tools like Natural Language Processing (NLP) and machine learning algorithms enable marketers to generate large-scale content, analyze audience preferences, and deliver tailored messages with unprecedented efficiency. The study aims to examine the benefits of AI in content marketing, including improved productivity, cost-efficiency, and enhanced targeting capabilities. At the same time, it addresses critical challenges, such as balancing automation with creativity, maintaining content authenticity, and mitigating the risks of algorithmic bias. Additionally, the research delves into the ethical considerations surrounding AI in content marketing, particularly with respect to data privacy and transparency in AI-generated content. By analyzing case studies and industry trends, this research provides insights into how businesses can effectively leverage AI for content marketing while overcoming the inherent challenges. Ultimately, it offers a comprehensive understanding of how AI can be harnessed to enhance marketing efforts in the digital age while maintaining a human touch and ethical standards. In addition to examining the operational and ethical dimensions, this research also investigates how AI is reshaping the role of marketers. As AI automates routine tasks such as keyword optimization, content scheduling, and performance tracking, marketers are free to focus on more strategic, creative, and high-value activities. However, this shift raises questions about the evolving skill sets required for marketers in an AI-driven landscape. The study explores how AI can complement human creativity rather than replace it, emphasizing the need for marketers to adapt to new tools while maintaining a strong foundation in storytelling, branding, and customer engagement. By highlighting both the technological and human aspects of AI integration, this research provides a balanced perspective on the future of content marketing in an increasingly automated world.

Geospatial data in AML risk management: A review of applications and best practices

This review explores the role of geospatial data in enhancing Anti-Money Laundering (AML) risk management, focusing on its applications, best practices, and strategic implications for organizations. Geospatial data, combined with advanced analytical tools, provides critical insights into transaction patterns, customer locations, and geographic risk factors, enabling financial institutions to detect suspicious activities more effectively. The paper examines how geospatial data integration supports compliance with regulatory frameworks, bolsters transaction monitoring systems, and facilitates enhanced due diligence processes. Through a systematic review of existing literature and case studies, this paper identifies key applications such as identifying high-risk jurisdictions, mapping transaction flows, and detecting cross-border money laundering schemes. Best practices are highlighted, including leveraging geospatial analytics in conjunction with machine learning algorithms, optimizing data governance, and ensuring interoperability across systems. The findings suggest that organizations adopting these practices are better equipped to combat money laundering by improving risk assessment, reducing false positives, and enhancing investigative efficiency. Additionally, the review discusses the challenges of data privacy, regulatory compliance, and the technical complexities associated with geospatial data integration. The paper concludes by outlining future prospects, recommending further advancements in real-time geospatial analytics, cross-sector collaborations, and the adoption of emerging technologies to strengthen AML frameworks. This review serves as a comprehensive guide for financial institutions seeking to incorporate geospatial data into their AML strategies, driving both compliance and operational efficiency.

Integrating Artificial Intelligence into ESG Practices: Opportunities, Challenges, and Strategic Solutions for Corporate Sustainability

Environmental, social, and governance (ESG) practices have become increasingly important in corporate strategy in recent years, while the rapid development of artificial intelligence (AI) has created new opportunities and challenges for corporate sustainability. AI technology driving companies for ESG time is getting more and more attention. This study examines the application of AI technologies in environmental management, social responsibility, and corporate governance, demonstrating their potential to optimize resource utilization, reduce carbon emissions, improve recruitment fairness, and prevent fraud. However, integrating AI with ESG faces many challenges, including technological complexity, high costs, data privacy and ethical issues, and organizational and cultural resistance. To address these challenges, this study proposes solutions to reduce financial burdens, secure data, and enhance cultural buy-in through strategies such as technology partnerships, open-source tools, and employee training. By delving into the convergence of AI and ESG, this study provides companies with a guiding direction to fully utilize the potential of AI while maintaining long-term sustainability.

RESPONSE OF LIQUID MANURES AS ALTERNATIVE NUTRIENT ON GROWTH AND YIELD OF TOMATO

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.

Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing

Artificial intelligence (AI) is revolutionizing digital marketing, with AI-driven personalization emerging as a powerful tool for shaping consumer experiences and driving engagement. This study explores how AI-powered technologies, such as recommendation engines, dynamic content delivery, and targeted advertisements, are transforming consumer behavior in the digital landscape. By leveraging big data, machine learning, and predictive analytics, marketers are now able to deliver hyper-personalized experiences that cater to individual preferences, leading to enhanced customer satisfaction and increased conversion rates. This research examines the impact of AI-driven personalization on key aspects of consumer behavior, including decision-making processes, brand loyalty, and purchase intentions. Additionally, it addresses the ethical challenges that arise from the use of AI in marketing, particularly around data privacy, algorithmic bias, and consumer trust. Through case studies from various industries, this study provides a comprehensive analysis of how personalization influences consumer psychology and purchasing patterns. Ultimately, the research offers valuable insights into the benefits and risks of integrating AI into marketing strategies, helping businesses understand how to harness the power of AI while maintaining ethical standards and safeguarding customer relationships in an increasingly digital world. Moreover, the study delves into the nuances of AI-driven personalization by exploring how consumers respond differently based on demographic factors such as age, gender, and cultural background. By analyzing customer data in real-time, AI allows for more accurate segmentation, ensuring that marketing messages are highly relevant and resonate with individual consumers. This level of precision not only increases customer engagement but also fosters a sense of exclusivity, making consumers feel more valued and understood by the brand. The research also investigates the role of AI in predicting future consumer behavior, using historical data to forecast trends and preferences, thereby enabling brands to stay ahead of the competition. As AI algorithms continuously learn and adapt, they can refine their recommendations, becoming more effective over time. However, there are concerns about over-reliance on AI, as excessive personalization may lead to "filter bubbles," where consumers are only exposed to a limited range of products or content, potentially stifling creativity and innovation.

Innovative Application of Big Data Technology in New Media Personalized Service

With the rapid development of big data technology, its application in the field of new media is becoming more and more extensive, especially in personalized service shows great potential. This paper discusses in depth the application, challenges and future development of big data technology in new media personalized service. The following conclusions are drawn: (1) Big data technology plays a key role in new media personalized service Big data technology can construct a refined user profile, realize personalized content recommendation, and improve user satisfaction and loyalty by collecting, analyzing and utilizing user behavioral data on new media platforms. (2) Big data technology faces multiple challenges in new media personalized services. Although big data technology shows great potential in new media personalized service, it still faces challenges in data privacy and security, data quality and accuracy, technology update and algorithm optimization, as well as regulatory compliance and ethics. (3) Innovative applications promote the development of big data technology in new media personalized services. With the continuous innovation and application of big data technology, new media personalized services are also developing and improving.

Transformative Impact of Convolutional Neural Networks on Healthcare, Autonomous Systems, and Global Technological Advancements

Convolutional Neural Networks (CNNs) have significantly impacted various industries by enabling machines to process visual data with unprecedented accuracy. This paper explores the transformative effects of CNNs on key sectors such as healthcare, autonomous systems, security, and environmental monitoring. In healthcare, CNNs are used in medical imaging for early diagnosis and treatment planning, enhancing telemedicine capabilities. In autonomous systems, CNNs enable real-time object detection and navigation, contributing to the development of safer, more efficient vehicles. In security, CNNs power facial recognition systems, raising both opportunities and ethical concerns regarding privacy and surveillance. Environmental monitoring benefits from CNNs through climate change research and wildlife conservation efforts, where they analyze vast amounts of data for critical insights. The paper also addresses the challenges posed by CNNs, including data privacy, security, and algorithmic bias, emphasizing the need for ethical standards and regulatory frameworks. Future advancements in CNN architectures and their integration with other AI models are expected to expand their applicability further and improve performance across different domains. This analysis underscores the dual importance of technological progress and ethical considerations to ensure that the benefits of CNNs are equitably distributed, contributing positively to global technological development and societal well-being.

A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System

Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal information. Input and output data of a learning model may include sensitive data that must be protected. Adversaries could intercept and exploit this data to infer more sensitive data or even to determine the structure of the prediction model. To guarantee data privacy, one option could be encrypting data and making inferences over encrypted data. This strategy would be challenging for learning models that now must receive encrypted data, make inferences over encrypted data, and deliver encrypted data. To address this issue, this paper presents a privacy-preserving machine learning approach using Fully Homomorphic Encryption (FHE) for a model that predicts risk levels of suffering a traffic accident. Despite the limitations of experimenting with FHE on machine learning models using a low-performance computer, limitations that are undoubtedly overcome by using high-performance computational infrastructure, we built some encrypted models. Among the encrypted models based on Decision Trees, Random Forests, XGBoost, and Fully Connected Neural Networks (FCNN), the model based on FCNN reached the highest accuracy (80.1%) for the lowest inference time (8.476 s).

Exploring the Security Challenges of an E-Voting System (EVS): A Case Study in the Bolgatanga Senior High School

The integration of electronic voting systems (EVS) into electoral processes promises increased efficiency, speed, and transparency. However, this technology also introduces a variety of security challenges that must be addressed to ensure the integrity of the voting process. This study explores the security vulnerabilities of an EVS, focusing on its implementation at Bolgatanga Senior High School (BIGBOSS). Through a case study approach, this research examines the potential threats to the administration of EVS, including issues related to data privacy, system integrity, authentication protocols, and susceptibility to cyber-attacks. A mixed-methods strategy was employed, utilizing surveys, interviews, and system tests to assess both technical vulnerabilities and user experiences. Key findings revealed Potential Voter Manipulation where unauthorized access could allow individuals to cast votes fraudulently; Insider Threats where trusted users exploiting their access could manipulate election outcomes; Weak Authentication; Lack of Robust Verification; Potential Physical Attack where individuals could vandalize machines and Vulnerabilities in Encryption which may expose votes, compromising ballot secrecy. The study recommends the implementation a blockchain-based technology , such as cryptographic techniques, consensus mechanisms, use of biometric verification to ensure the identity of voters as well as the use of digital signatures to verify the authenticity of electronic ballots and ensure that they haven't been altered during transmission, implementation of real-time monitoring of the e-voting system to detect and respond to security threats promptly” and also provide ongoing cybersecurity education and training to election officials, staff, and voters to raise awareness and reduce the risk of human error. These findings contribute to the ongoing discourse on EVS security, providing insights into practical measures for improving the reliability and safety of electronic voting systems in educational institutions and beyond.

The Advantages of Health Recommending System with Wearable Devices

The health recommendation system is one of the critical application scenarios of machine learning technology. With the advantages of wearable and mobile devices to collect rich user health monitoring data, machine learning algorithms can accurately advise users’ health status and corresponding living habits. However, some technical and regulatory challenges still need to be solved at this stage. Specifically, to ensure real-time recommendation results, the health recommendation system needs to adopt the method of training computational learning algorithms on wearable devices. However, due to wearable devices’ limited computing resources and storage space, it is often difficult to obtain satisfactory results by changing the strategy. In addition, the issue of data privacy deserves attention because wearable devices collect a lot of user privacy information, but the data manager is often not the user himself. Finally, Bluetooth protocol usually connects wearable devices, making them vulnerable to intentional attacks. This paper investigates the above problems and expounds on the solutions to related problems.

Three-Dimensional Printing Technology in Drug Design and Development: Feasibility, Challenges, and Potential Applications

Background/Objectives: The present investigation evaluates the impact of 3D-printing technology on the design of pharmaceutical drugs, considering the feasibility issues and problems concerning technological, pharmaceutical, and clinical matters. This paper aims to review how 3D printing can modify the traditional manufacturing of drugs with personalized medicine-therapy outcomes being individualized and optimized, hence improving patients’ compliance. Methods: The historical development of 3D printing from rapid prototyping to advanced pharmaceutical applications is discussed. A comparison is then made between traditional drug manufacturing approaches and the different techniques of 3D printing, including stereolithography, material extrusion, and binder jetting. Feasibility is assessed based on clinical trials and studies evaluating the efficacy, safety, bioavailability, and cost-effectiveness of 3D-printed drugs. Results: Current evidence indicates that material selection, regulatory barriers, and scalability issues are some of the major challenges to be overcome for wider acceptance. Other matters, such as ethical issues concerning patient data privacy, the misuse of 3D-printing technology, and technical complexities related to pharmaceutical 3D printing, are discussed further. Future applications also include bioprinting and in situ printing together with their implications for personalized drug delivery, which will also be discussed. Conclusions: This review stresses that intersectoral collaboration and the updating of regulatory frameworks are a must to overcome the barriers that confront 3D-printing applications in drug development. can could be an opportunity for innovative licensing and manufacturing techniques in pharmaceutical product development that can change the paradigm of personalized medicine through modern printing techniques.

Application of Artificial Intelligence Technology in New Energy Vehicles

Due to environmental pollution caused by excessive exploitation of world resources, countries are now advocating for green travel, which has led to the rapid development of the new energy vehicle industry. Compared to traditional cars, the safety, maneuverability, and convenience of new energy vehicles have made the application of artificial intelligence technology increasingly widespread in this field. Therefore, this article provides a systematic review and analysis of the application of artificial intelligence technology in new energy vehicles. Firstly, the article outlined the main application scenarios of artificial intelligence in the field of new energy vehicles, including autonomous driving, energy management optimization, vehicle intelligent interconnection, and predictive maintenance. On this basis, this paper analyzed the role of artificial intelligence technology in improving the performance, safety, and reliability of new energy vehicles, as well as the technological challenges and development trends it faces. For example, issues such as data privacy protection and real-time performance need to be further addressed. In the future, artificial intelligence and new energy vehicles will further integrate, making important contributions to achieving intelligent, green, and safe future travel.

The Economic Impact of Successful Businesspeople: The Dual Effect of Wealth Creation and Spending

This article explores the complex impact of successful businesspeople on others and society through wealth creation and spending in their economic lives. The article begins by defining the concept of a “successful businesspeople” and points out its diversity in different historical and political contexts. Then, from an economic perspective, this paper analyzes how successful businesspeople can positively impact society by promoting economic growth, improving production efficiency, creating jobs, and donating to philanthropy in their different roles as investors, producers, consumers, donors, and employers. However, the article also points out the negative externalities that can be brought about by successful businesspeople, such as environmental damage, labor problems, data privacy violations, and public health risks. By citing historical and modern examples, such as Andrew · Carnegie, Henry · Ford, Jeff · Bezos, and Elon · Musk, this article further elaborates on how the economic behavior of successful businessmen has both positive and negative effects, and emphasizes the importance of understanding these complex relationships.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Preferences of parents from diverse backgrounds on genomic screening of apparently healthy newborns.

Genomic sequencing has been proposed as a strategy to expand newborn screening. Perspectives on genomic newborn screening from parents of diverse racial, ethnic, and socioeconomic backgrounds are needed to shape equitable implementation of this modality. We conducted 20 semi-structured interviews (15 English, 5 Spanish) and seven focus groups (4 English, 3 Spanish) with parents from diverse backgrounds to assess their perspectives regarding which disorders and variants might be screened, data privacy, and barriers to pursuing specialized care. Parents felt that genomic newborn screening would provide them with improved understanding of their children's health and had the potential to yield health and personal benefits. Themes that became evident included: interest in childhood and family health risks, the value of emotional preparation and personal planning, understanding of uncertain and low-risk results, concerns regarding data privacy, and concerns about support following the receipt of a positive newborn screening result. The expected benefits and concerns expressed by parents of diverse backgrounds regarding genomic newborn screening should guide future policy decisions. Their preferences should be considered prior to the implementation of large-scale genomic newborn screening programs.