Privacy Regulations Research Articles

Digital Privacy Rights in the Age of Big Data: Balancing Security and Civil Liberties

In the era of big data, the protection of digital privacy rights has become a paramount concern, requiring a delicate balance between security measures and civil liberties. This article explores the challenges and complexities surrounding digital privacy in the age of big data and examines the strategies and policies aimed at safeguarding individuals' privacy rights. Through qualitative methods such as literature review and library research, this study delves into the multifaceted nature of digital privacy rights and the implications of big data analytics on personal data protection. The literature review reveals the rapid proliferation of digital technologies and the extensive collection, storage, and analysis of personal data by governments, corporations, and other entities. While big data analytics offer numerous benefits, including enhanced security and personalized services, they also raise significant concerns regarding privacy intrusion, surveillance, and data breaches. Key themes explored in this study include the evolution of privacy laws and regulations, the role of encryption and data anonymization techniques in preserving privacy, and the ethical considerations surrounding data usage and consent. Additionally, the article examines the impact of emerging technologies such as artificial intelligence and the Internet of Things on digital privacy rights. Furthermore, the study discusses the importance of transparency, accountability, and user control in ensuring the protection of digital privacy rights. It also highlights the need for collaborative efforts between governments, industry stakeholders, and civil society to develop robust privacy frameworks that uphold both security and civil liberties in the digital age. In conclusion, as the volume and complexity of digital data continue to grow exponentially, safeguarding digital privacy rights remains a pressing challenge. By adopting a comprehensive approach that balances security imperatives with respect for individual freedoms, society can navigate the complexities of the digital landscape while preserving fundamental rights to privacy and autonomy.

Maximizing Artificial Intelligence for Patient Satisfaction: Marketing Strategies in The Digital Health Era

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>The advent of the Fourth Industrial Revolution has catalyzed transformative changes in the healthcare sector, highlighting the need to optimize Artificial Intelligence (AI) to improve patient satisfaction. This research examines the role of AI in revolutionizing the healthcare market and its potential to improve patient outcomes. Using a systematic literature review, we collected and analyzed studies published from 2017 to 2022 from various databases such as ProQuest, Science Direct, PubMed, CINAHL, and Scopus, focusing on AI applications in healthcare and its impact on patient satisfaction. Inclusion criteria for the initial screening were articles that were freely available in full text, had open access, and were published in English or Indonesian, as well as ensuring no duplication in the records found. Significant findings were synthesized through thematic and descriptive analysis to ascertain the efficacy of AI in patient care. Of the 25 articles relevant for further evaluation, 12 studies met the inclusion criteria and were included in the qualitative analysis. The chosen methodology allowed the authors to conduct a comprehensive and systematic review. A practical suggestion that can be drawn from these findings is the importance of investing in the development of artificial intelligence (AI) in healthcare. This requires adequate training for staff to understand and use these technologies effectively. In addition, healthcare institutions should ensure that the use of AI remains compliant with applicable privacy and ethical regulations. Collaboration between various parties such as healthcare institutions, technology providers, and researchers is also necessary to accelerate innovation and knowledge exchange. By utilizing real-time data analysis powered by AI, healthcare can be improved, and governments can support innovation in AI by providing incentives, supportive regulations, and adequate research funding.</em><em> </em></p><p> </p></td></tr></tbody></table></div><p><strong><em>Keywords: Artificial Intelligence, Digital Transformation, Healthcare Revolution, Patient Satisfaction, Service Optimization</em></strong></p>

Advancing Korean nationwide registry for hepatocellular carcinoma: a systematic sampling approach utilizing the Korea Central Cancer Registry database.

Hepatocellular carcinoma (HCC) presents a substantial public health challenge in South Korea as evidenced by 10,565 new cases annually (incidence rate of 30 per 100,000 individuals), in 2020. Cancer registries play a crucial role in gathering data on incidence, disease attributes, etiology, treatment modalities, outcomes, and informing health policies. The effectiveness of a registry depends on the completeness and accuracy of data. Established in 1999 by the Ministry of Health and Welfare, the Korea Central Cancer Registry (KCCR) is a comprehensive, legally mandated, nationwide registry that captures nearly all incidence and survival data for major cancers, including HCC, in Korea. However, detailed information on cancer staging, specific characteristics, and treatments is lacking. To address this gap, the KCCR, in partnership with the Korean Liver Cancer Association (KLCA), has implemented a systematic approach to collect detailed data on HCC since 2010. This involved random sampling of 10-15% of all new HCC cases diagnosed since 2003. The registry process encompassed four stages: random case selection, meticulous data extraction by trained personnel, expert validation, anonymization of personal data, and data dissemination for research purposes. This random sampling strategy mitigates the biases associated with voluntary reporting and aligns with stringent privacy regulations. This innovative approach positions the KCCR and KLCA as foundations for advancing cancer control and shaping health policies in South Korea.

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a substantial amount of data for finetuning. However, collecting and centralizing training data from diverse sectors becomes challenging due to distinct privacy regulations. Federated Learning (FL) emerges as a promising solution, enabling multiple clients to collaboratively train neural networks without centralizing their local data. To alleviate client computation burdens and communication overheads, previous works have adapted Parameter-efficient Finetuning (PEFT) methods for FL. Hereby, only a small fraction of the model parameters are optimized and communicated during federated communications. Nevertheless, most previous works have focused on a single modality and neglected one common phenomenon, i.e., the presence of data heterogeneity across the clients. Therefore, in this work, we propose a finetuning framework tailored to heterogeneous multi-modal FL, called Federated Dual-Aadapter Teacher (FedDAT). Specifically, our approach leverages a Dual-Adapter Teacher (DAT) to address data heterogeneity by regularizing the client local updates and applying Mutual Knowledge Distillation (MKD) for an efficient knowledge transfer. FedDAT is the first approach that enables an efficient distributed finetuning of foundation models for a variety of heterogeneous Vision-Language tasks. To demonstrate its effectiveness, we conduct extensive experiments on four multi-modality FL benchmarks with different types of data heterogeneity, where FedDAT substantially outperforms the existing centralized PEFT methods adapted for FL.

Digital firm regulation strategy with privacy data protection costs

AbstractAlthough the significant effects of protecting users' private data in the era of the digital economy have been recognized, digital firm regulation strategies based on privacy data protection costs remain unclear. We propose a theoretical model in which users have different perceived risks related to their personal data disclosure. We characterize how the revenue model of a digital firm shapes its optimal data regulation strategies. We show that the optimal digital service and profit have a positive correlation with the marginal value of two types of user privacy data and a negative correlation with regulatory costs. In addition, the digital firm's optimal digital services and profits are lowest under data privacy regulation at a cost for all users. More important, through the regulation of the social planner, digital firms both improve their digital services and enhance user welfare, which truly achieves the purpose of a “win‐win” outcome.

A Privacy-Compliant Process for Digital Forensics Readiness

This research paper examines the issue of privacy compliance in digital forensics readiness, specifically in relation to the breach of confidentiality during analysis of collected data on users. This is a problem because the collection and analysis of digital evidence during these investigations can have a potential impact on individuals’ privacy rights. The study’s methodology involves a literature review of relevant research, an analysis of privacy regulations, and a case study of a real-world digital forensics investigation. The main findings of the study indicate that organizations need to develop and implement robust privacy measures and data protection policies to ensure that their digital forensics readiness efforts are privacy compliant and do not compromise user privacy. Some examples of why this is necessary are provided in the research to address these privacy compliance issues, this study proposes a measure, this measure implements technical safeguards to protect user data and maintains its confidentiality. By implementing the proposed measures, organizations can maintain their digital forensics readiness while also protecting user privacy.

Impact of privacy regulation involving information collection on the ride-hailing market

Ride-hailing platforms can indirectly adjust privacy-sensitive customers’ demand via pricing and information collection strategies while encouraging drivers to provide services by offering an attractive wage rate. Since the privacy information collected by platforms brings some negative effects, governments enact privacy regulations to protect customer privacy. Inspired by this observation, our paper aims to investigate the impact of privacy regulations on different stakeholders in the ride-hailing market, including customers, drivers, platforms, and regulators. Specifically, we formulate two multi-stage Stackelberg models under the scenarios with/without privacy regulation, respectively, compare the equilibrium results under these two scenarios, and obtain several interesting findings. First, the partial-coverage pricing strategy is more likely to be adopted by platforms than the full-coverage pricing strategy as privacy regulations are enforced. Second, when customers are not too severely disturbed by advertising, the privacy regulation transfers information decision-making right from the ride-hailing platform to customers and utilizes customers’ self-interest to alleviate the negative impact of information collected by the platform; Otherwise, it seems somewhat “redundant”. Third, effective regulation improves information security at the expense of benefits of stakeholders (the customer, drivers, and platforms). Therefore, the decision-making about privacy regulation results from a trade-off between economic benefits and information security, suggesting that regulators should enforce privacy regulation only if the level of externalities is high enough. This meaningful criterion can provide theoretical support for the decision-making of regulators.

Between Privacy and Utility: On Differential Privacy in Theory and Practice

Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be translated into real-world systems, which—if left unexamined and unrealized in practice—could function to shield data collectors from liability and criticism, rather than substantively protect data subjects from privacy harms. This article investigates these assumptions and discusses their implications for using DP to govern data-driven systems. In Parts 1 and 2, we introduce DP as, on one hand, a mathematical framework and, on the other hand, a kind of real-world sociotechnical system, using a hypothetical case study to illustrate how the two can diverge. In Parts 3 and 4, we discuss the way DP frames privacy loss, data processing interventions, and data subject participation, arguing it could exacerbate existing problems in privacy regulation. In part 5, we conclude with a discussion of DP’s potential interactions with the endogeneity of privacy law, and we propose principles for best governing DP systems. In making such assumptions and their consequences explicit, we hope to help DP succeed at realizing its promise for better substantive privacy protections.

Nurses on Social Media: Global Challenges and Pakistani Context.

Social media has become a global phenomenon, impacting the nursing profession worldwide. While nurses on social media face universal challenges, each country also has unique factors that influence the nature of these challenges. This editorial highlights the global challenges nurses face on social media and discusses the specific context of Pakistan, shedding light on the challenges experienced by nurses in this country. Nurses worldwide face the challenge of protecting patient confidentiality and privacy when using social media. The global nature of social media platforms means that nurses must be mindful of privacy laws and regulations specific to their country. Ensuring adherence to ethical guidelines and implementing stringent privacy measures is crucial to safeguard patient information across borders1.
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The impacts of multiple privacy regulations and national security infrastructure on health information exchange: a study of hospitals across Europe

PurposeSharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.Design/methodology/approachThe study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.FindingsThe study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.Originality/valueThis study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

Where you go is who you are: a study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

FedKNN: Secure Federated k-Nearest Neighbor Search

Nearest neighbor search is a fundamental task in various domains, such as federated learning, data mining, information retrieval, and biomedicine. With the increasing need to utilize data from different organizations while respecting privacy regulations, private data federation has emerged as a promising solution. However, it is costly to directly apply existing approaches to federated k-nearest neighbor (kNN) search with difficult-to-compute distance functions, like graph or sequence similarity. To address this challenge, we propose FedKNN, a system that supports secure federated kNN search queries with a wide range of similarity measurements. Our system is equipped with a new Distribution-Aware kNN (DANN) algorithm to minimize unnecessary local computations while protecting data privacy. We further develop DANN*, a secure version of DANN that satisfies differential obliviousness. Extensive evaluations show that FedKNN outperforms state-of-the-art solutions, achieving up to 4.8× improvement on federated graph kNN search and up to 2.7× improvement on federated sequence kNN search. Additionally, our approach offers a trade-off between privacy and efficiency, providing strong privacy guarantees with minimal overhead.

Privacy regulation and firm performance: Estimating the GDPR effect globally

AbstractThis paper examines how privacy regulation shaped firm performance. Controlling for firm and country‐industry‐year unobserved characteristics, we compare the outcomes of firms at different levels of exposure to EU markets, before and after the enforcement of the General Data Protection Regulation (GDPR) in 2018. We find that the GDPR had the unintended consequence of harming the profitability of companies targeting European consumers through the cost channel. Technology firms experienced a 2.1% decline in profits, but not in sales. The GDPR increased extra expenses, added to firms wage bills, and accelerated patenting in GDPR‐related technology fields. The main burdens have been borne by smaller companies.

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

KAM-PaY: Closed Wallet with SQL-Technology and Biometric Authentication

Closed Wallet is a novel database collection system designed to securely gather and manage user data using SQL technology. This system addresses the growing concern for data privacy and security by implementing robust authentication methods and access controls. Through SQL queries, Closed Wallet efficiently retrieves and stores user information while adhering to stringent privacy regulations. The proposed KAM-Pay (Kamaraj payment), an implementation of the closed e-wallet system for the college canteen in Kamaraj Engineering college, is being developed using Visual Studio. Furthermore, the biometric authentication in proposed work ensures the data integrity and confidentiality, safeguarding sensitive information from unauthorized access.

Privacy‐preserving WiFi‐based crowd monitoring

AbstractThe process of estimating the number of individuals within a defined area, commonly referred to as people counting, is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well‐informed decision‐making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart‐device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR. Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy‐preserving mechanisms. Importantly, it seamlessly integrates with trajectory‐based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights.

Digital communication tools in family therapy: A review of opportunities and challenges for English-Speaking practitioners

Digital communication tools have revolutionized the field of family therapy, offering both opportunities and challenges for English-speaking practitioners. With the proliferation of technology, therapists have access to a wide array of platforms and applications that can enhance therapeutic interventions. However, integrating these tools into practice requires careful consideration of ethical, legal, and clinical implications. Digital communication tools offer numerous advantages for family therapy practitioners. Teletherapy platforms enable therapists to reach clients in remote locations or those with mobility constraints, expanding access to care. Video conferencing allows for face-to-face interactions, facilitating rapport building and nonverbal communication cues essential for effective therapy. Additionally, digital tools such as secure messaging apps or virtual whiteboards enhance communication between sessions, fostering continuity of care and homework completion. Online support groups and forums provide clients with opportunities for peer support and community engagement, supplementing traditional therapy approaches. Despite their benefits, digital communication tools pose significant challenges for English-speaking practitioners. Ensuring confidentiality and data security is paramount, requiring adherence to stringent privacy regulations such as HIPAA (Health Insurance Portability and Accountability Act). Therapists must navigate issues of informed consent, boundary maintenance, and emergency protocols in the digital realm. Moreover, technological barriers, such as internet connectivity issues or lack of digital literacy among clients, can hinder the effectiveness of therapy sessions. Cultural considerations also play a role, as language barriers or cultural differences in technology use may impact therapeutic engagement. Digital communication tools offer English-speaking family therapy practitioners unprecedented opportunities to enhance therapeutic interventions. However, these tools come with inherent challenges that must be addressed to ensure ethical and effective practice. By carefully navigating the opportunities and challenges presented by digital communication tools, therapists can harness the power of technology to improve client outcomes and advance the field of family therapy.

GDPR's impact on cybersecurity: A review focusing on USA and European practices

The General Data Protection Regulation (GDPR) has emerged as a landmark legislation reshaping the global landscape of data privacy and cybersecurity. Enforced in May 2018, the GDPR has had a profound impact on organizations worldwide, prompting a reevaluation of cybersecurity practices to ensure compliance with stringent data protection standards. This paper provides a comprehensive review of GDPR's influence on cybersecurity, with a particular emphasis on the contrasting approaches and practices adopted in the United States (USA) and Europe. The GDPR introduces a set of robust principles designed to protect the rights and privacy of individuals, emphasizing the need for transparency, accountability, and proactive measures to safeguard personal data. Its extraterritorial scope extends its impact beyond European borders, compelling businesses operating globally to adhere to its regulations. This paper explores the challenges and opportunities arising from GDPR compliance, examining how organizations in the USA and Europe have navigated the evolving cybersecurity landscape. In the USA, where privacy regulations historically differed across states, the GDPR has prompted discussions around the development of federal privacy laws. The review delves into the varying approaches adopted by American businesses, considering the interplay between state and federal regulations in shaping cybersecurity strategies. Conversely, European practices reflect a proactive response to the GDPR, as organizations have embraced the principles embedded in the regulation to fortify cybersecurity frameworks. The paper investigates the evolution of cybersecurity standards in Europe, highlighting successful strategies and potential areas for improvement. By synthesizing experiences from both sides of the Atlantic, this review contributes to a deeper understanding of the GDPR's impact on cybersecurity. It sheds light on the evolving dynamics of data protection, offering insights for organizations seeking to enhance their cybersecurity resilience in the face of a rapidly changing regulatory landscape.

A comparative study of federated learning methods for COVID-19 detection

Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.

A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.