Privacy Of Personal Data Research Articles

GEOGRAPHICAL INFORMATION SYSTEM: APPLICATION TO THE GEOLOCATION OF THE NEAREST HEALTH FACILITIES IN THE TOWN OF KANANGA

New technology now makes it possible to determine the location of an object or person with a certain degree of accuracy. This technology is generally based on the GPS system or the communication interfaces of a mobile phone. Geolocation has a wide range of applications and purposes, from navigation assistance and personal contact to real-time management of human and vehicle resources for individuals or companies. This work, linked to mobile applications, shows how a well-defined entity can be located if, for example, it goes missing. Today, new technology remains an essential tool in geographic information systems. Every user is expected to have precise knowledge of how to use geolocation when necessary. The tool chosen for this work can be a smartphone, a tablet, an Android phone or any other model the user chooses. The user enters a reference and, by pressing the search button, a new window opens showing the structure closest to his position and the steps he should take to reach his destination as quickly as possible. Once the destination has been found, the user simply selects the shortest route to get there. Then there is the issue of geolocation, something that should never be forgotten. First and foremost, geolocation raises the issue of personal data protection and privacy. The importance of the application that has been put in place is to help anyone who doesn't know a place that is sometimes remote, without having to make a lot of effort or go through a person, but he ends up getting there, or discovering someone and something else.

A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients’ right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients’ private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.

Pedagogical and Ethical Implications of Artificial Intelligence in EFL Context: A Review Study

In the contemporary world, technology is advancing and being integrated in various sectors, impacting human lives in many ways. When the conversation on technological advancements emerges, one of the most prevalent topics is artificial intelligence (AI). AI has gradually developed into an integral part of human lives, with its application being common in finance, healthcare, security, and education. In education, AI can be integrated into English as a Foreign Language (EFL), leading to the introduction of a dynamic realm with profound ethical and pedagogical dimensions. The current study focuses on the interaction between EFL education and AI technologies by analyzing the obstacles and opportunities that might emerge. Pedagogically, AI has multiple advantages to any EFL education setting, which include targeted feedback, automated grading, and personalization of learning experiences, especially for learners with disabilities. However, the use of AI leads to some concerns, including the exclusion of the chance for learning to engage in creative and critical thinking. It is also associated with the possible dehumanization of the learning process and biases that might result from the use of AI software. Also, using AI in an EFL setting raises various ethical concerns, including personal data privacy, academic dishonesty, and a decline in job security for teachers. When teachers do not feel that their jobs are safe, their motivation is likely to decline. Also, using AI in an EFL setting raises concerns such as the loss of cultural nuances and an unhealthy reliance on technology. Thus, there needs to be a balance whenever AI is used in an EFL education setting for the sake of protecting educational objectives and adhering to the ethical standards expected of such settings. This paper highlights the use and impact of AI on EFL pedagogically and the risks and ethical concerns associated with such adoptions. The study is based on the understanding that the multiple benefits associated with the use of AI in education come with challenges that necessitate a balanced approach to implementation.

Current challenges and the future of legal protection of personal data: under the influence of digitalization development

Current challenges and the future of legal protection of personal data: under the influence of digitalization development

Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study.

Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.

Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

Anonymization techniques are widely used to make personal data broadly available for analytics/data-mining purposes while preserving the privacy of the personal information enclosed in it. In the past decades, a substantial number of anonymization techniques were developed based on the famous four privacy models such as k-anonymity, ℓ-diversity, t-closeness, and differential privacy. In recent years, there has been an increasing focus on developing attribute-centric anonymization methods, i.e., methods that exploit the properties of the underlying data to be anonymized to improve privacy, utility, and/or computing overheads. In addition, synthetic data are also widely used to preserve privacy (privacy-enhancing technologies), as well as to meet the growing demand for data. To the best of the authors’ knowledge, none of the previous studies have covered the distinctive features of attribute-centric anonymization methods and synthetic data based developments. To cover this research gap, this paper summarizes the recent state-of-the-art (SOTA) attribute-centric anonymization methods and synthetic data based developments, along with the experimental details. We report various innovative privacy-enhancing technologies that are used to protect the privacy of personal data enclosed in various forms. We discuss the challenges and the way forward in this line of work to effectively preserve both utility and privacy. This is the first work that systematically covers the recent development in attribute-centric and synthetic-data-based privacy-preserving methods and provides a broader overview of the recent developments in the privacy domain.

Population Health Management: principles, models and areas of application in public health.

There is no single model for Population Health Management (PHM) and different definitions have been proposed. All PHM models and definitions share the overall aim of improving population health and reduce healthcare costs. To achieve these objectives, PHM makes use of conceptual tools such as the Chronic Care Model and predictive medicine, and technical tools such as information systems and computational and record-linkage techniques to collect and analyse data. Using these tools, it makes it feasible to articulate PHM approaches in the following steps: identification of a population, stratification of individuals according to risk levels, mapping of health needs and development of targeted interventions and models of care. PHM has been applied in a variety of national and regional settings, proving to have great potential. However, the success of PHM models depends on a number of factors. In particular, few key points have emerged that must be taken into consideration when planning and implementing PHM programs. They include PHM funding schemes, strategies to ensure people adherence, the equity dimension in its multiple aspects, and the privacy of personal data. In addition to these challenges, there is the need to act in a legislative context appropriate to the implementation of PHM.

Personal Data Privacy vs Public Interest

Governments around the world have gathered masses of personal information on their citizens as part of the fight against the Covid pandemic. Citizens, willingly for the most part, yielded such data in order to protect the public good and safety of society. Focusing on personal data gathering, processing and protection for public good, the authors consider how far citizens are willing to accept that their personal data can be collected by governments during a public health crisis. The situation in Europe and in China shall be compared, showing how the “public interest” during Covid-19 was understood very differently in different jurisdictions.

Personal Data Privacy in the Age of Artificial Intelligence

The article discusses the relationship between Artificial Intelligence (AI) and personal data privacy. Artificial Intelligence which emerged in the 1950s with Alan Turing's question "Can Machines Think?" has achieved significant successes in recent years, particularly in the development of human-like machine designs. The advancement of AI is closely related to the collection of big data, which in turn involves gathering more personal data. In today's context, marked by increased philosophical discussions about freedom and heightened emphasis on the individual, the issue of personal data privacy has become considerably important, accompanied by numerous challenges. Despite the existence of many national and international legal regulations regarding data privacy, there lacks a regulation overseeing the development processes of related technologies. Artificial Intelligence is often classified into categories based on its technical capabilities, namely "Narrow AI," "General AI," and "Superintelligence." AI has attracted significant attention in philosophy and has brought about interdisciplinary research in fields ranging from logic and aesthetics to law, ethics, medicine, and even art. In the face of emerging technologies, it seems inevitable that philosophy will initiate new research trajectories. The paper examines these three concepts related to AI, their connection with the concept of privacy, the issues arising and potentially arising in the realm of personal privacy, and the measures to mitigate these risks. The methodology employed includes a literature review and qualitative data analysis. The research reveals that the notion of privacy is mainly linked to ensuring the security of personal data related to technological advancements and the changing perception of privacy in individuals. However, it's observed that these studies often do not encompass current developments. The paper endeavors to answer questions such as: What do the terms "AI, Narrow AI, General AI, Superintelligence, and Personal Data Privacy" refer to? Have studies been conducted on the relationship between AI concepts and personal data privacy? What are the risks associated with personal data privacy, and how can these risks be mitigated? The study aims to highlight the risks in the development processes of AI as increasingly vocalized by scientists working in this field. It delves into issues such as the collection and storage of personal data, often unbeknownst to individuals, through cloud technologies and the potential problems arising from the use of this data in AI training. The article also discusses potential solutions to these issues. Drawing attention to recent developments in this scope is of paramount importance for social scientists, and disseminating information about these advancements is crucial. Based on the examinations and analyses conducted, it's determined that AI, now possessing new competencies, requires an interdisciplinary approach to address the areas it influences. It's found that leaving this field solely to engineers would result in numerous problems in the near future. Safeguarding privacy in the face of intelligent machines that encompass every aspect of our lives is a formidable challenge. Reducing potential problems necessitates individual awareness, legal regulations concerning AI development processes, and recognizing that AI technologies causing issues can also be part of the solution.

Harnessing Artificial Intelligence in Academic Writing: Potential, Ethics, and Responsible Use

Dear Editors, I read your editorials with interest [1,2]. Artificial intelligence has become increasingly popular in recent years. Moreover, it is seen that valuable comments made on the article focus on issues such as potential harms in the academic use of artificial intelligence, publication ethics, and the ontological position of artificial intelligence [3-6]. As with other technological developments in our life, artificial intelligence seems to be increasingly present in our lives [7]. The use of artificial intelligence in article writing is a current topic of discussion. Writing an article from scratch with artificial intelligence doesn't seem quite right. The fact that artificial intelligence doesn't have an entirely independent thought structure, and that it obtains its information only based on the web, could lead to plagiarism. Also, it will not be fully understood whether it provides correct references and whether it violates the privacy of personal data. Furthermore, we do not find it correct to accept it as a co-author. Authorship carries a responsibility that an AI tool, no matter how advanced, cannot bear. Conversely, artificial intelligence will be incapable of assuming responsibility. So, should we stay away from artificial intelligence in this case? We believe the answer to this question is no. We can use artificial intelligence while writing an article, and it can facilitate our work. Finding a title after writing the article is an important issue. It must be impressive, and some journals have a character or word limit for the title. Artificial intelligence can help us in finding a title. Artificial intelligence can also assist in creating an abstract. Each journal has different rules about the abstract section. When rejected from one journal, it's necessary to create a new abstract for another journal. Artificial intelligence can speed up this process. When the article is ready, it needs to be checked for grammar and spelling rules. Artificial intelligence will be very useful in this situation. Preparing a good cover letter is important to attract the editor's attention. Artificial intelligence will assist in writing the cover letter. Although we think that artificial intelligence cannot be a co-author and cannot be used to write an article from scratch, we believe that we can benefit from artificial intelligence while writing our article. Using artificial intelligence will save us time. It will also help us write higher quality articles. In conclusion, artificial intelligence is now a part of our lives. Instead of avoiding it, we believe that we need to take advantage of artificial intelligence in a way that will facilitate our work. Sincerely yours,

Cybersecurity in health sector: a systematic review of the literature

Currently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors.

Children’s rights and personal data protection in the jurisprudence of the European Court of Human Rights

This article analyses the jurisprudence of the European Court of Human Rights (ECtHR) in order to identify and characterise the argumentative conclusions produced by the Court in their decisions on personal data protection and that affect children’s rights. The data collection technique consisted of a search and selection of the arguments in the Court’s judgments on situations that refer to violations of personal data. For the argumentative analysis, through an argumentation scheme, a judgment of a case that refers to child protection against sexual online exploitation was selected. In this context, we have observed that the Court structured its arguments to verify the violation of privacy and protection of personal data using the following topics: (i) respect for private life; (ii) criminal-law provisions; and (iii) positive obligations. The use of these topics in the subsequent judgments of the ECtHR is also identified through the citations to justify their own decisions.

UBO INFORMATION TRANSPARENCY IN THE CORPORATE REGISTERS AND ITS EFFECT ON PRIVACY PROTECTION FROM AML-CFT PERSPECTIVE

This paper examines the essence of standards set by the European Union legislation, regulating two areas of law which are mutually opposite but vastly interconnected in practice, particularly from the perspective of AML/CFT (Anti-Money Laundering/Combating the Financing of Terrorism). Identifying the exact position of the thin line that distingushes between the right to free access to information and the right to protect the privacy of personal data is of great importance and has a legitimate purpose because the complexity of schemes aimed at attaining the set goals can easily be misused to conceal the violation of a basic human right in practice. This paper analyses the legitimicy of complete transparency of personal data of the actual (direct or indirect) owner as the basic tool for preventing the abuse of the financial system for money laundering and terrorism financing purposes. This paper attempts to explain and correlate the two aforementioned rights by analysing them from the perspective of the Ultimate Beneficial Owner (UBO).

Направления исследований в области анализа образовательных данных в высшей школе: теоретический обзор

The paper provides an overview of foreign English-language research literature on pedagogy, the aim of which is to identify the most relevant lines of research in the area of educational data mining in modern higher education. The review considers the factors that have caused the development of educational data mining (hereinafter EDM) and learning analytics (hereinafter LA) in the context of digital transformation processes in modern society. The paper discusses the potential, problems and directions of implementing EDM and LA in higher education in general, as well as in the field of academic performance and students’ behavior, educational programs development and improving the education system efficiency. Scientific novelty of the review lies in identifying the most relevant tasks for EDM research and defining advanced research directions in this area for all actors of educational process in higher education. As a result, the authors analyzed research papers on the described subject area published in the period from 2017 to 2023 and described issues related to personal data ethics and privacy in the context of EDM implementation, the relevant methods of EDM, the experience of EDM implementation in higher education.

A Privacy-Preserving Framework Using Hyperledger Fabric for EHR Sharing Applications

Electronic Health Records, or EHRs, include private and sensitive information of a patient. The privacy of personal healthcare data can be protected through Hyperledger Fabric, a permissioned blockchain framework. A few Hyperledger Fabric- integrated EHR solutions have emerged in recent years. However, none of them implements the privacy-preserving techniques of Hyperledger Fabric to make transactions anonymous or preserve the transaction data privacy during the consensus. Our proposed architecture is built on Hyperledger Fabric and its privacy-preserving mechanisms, such as Identity Mixer, Private Data Collections, Channels and Transient Fields to securely store and transfer patient-sensitive data while providing anonymity and unlinkability of transactions.

Taiwanese Media reporting COVID-19 disinformation crisis: A mixed-method study of technological epidemic prevention regarding health personal data, privacy and security issues

<p>本混合法研究檢視2020年初臺灣媒體報導新冠肺炎疫情假訊息內容，分析數位防疫之個資、隱私與資安議題呈現趨勢及新聞框架運用。大數據分析顯示：有別於西方報導，臺灣疫情假訊息新聞甚少關注防疫個資與隱私，對資安議題較為重視。公衛危機新聞框架內容分析結果發現：科技防疫報導最常用行動框架，後果與保證框架次之，負面框架（衝突與不確定）較少。個資、隱私和資安報導，及大部分新聞框架選擇，隨著疫情時期變化，呈現顯著性差異。</p> <p> </p><p>As COVID-19 was the first global pandemic of the digital age, many countries utilized information technologies and big data as technological epidemic prevention measures which led to controversies regarding personal data misuse, privacy invasion, and security risks. The mixed-method study used big data and content analysis to examine Taiwanese news reports related to personal data, privacy, and security issues in early COVID-19 outbreak. In contrast to Western studies, the results revealed that Taiwanese media reports, under infodemic influences, emphasized the importance of data security over that of protecting personal data and data privacy. Based on news framing of public health crisis, content analysis results showed the dominant use of action framing was followed by consequence and assurance framing, whereas negative framing (conflict and uncertainty) was rarely employed. The news reports about personal data, privacy, and security found statistical differences among three epidemic periods, as did their selection of news framing.</p> <p> </p>

PDPChain: A Consortium Blockchain-Based Privacy Protection Scheme for Personal Data

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

The Right to Privacy Based on the Law of the Republic of Indonesia Number 27 of 2022

The impact of technological advances and digital activities in society today affects a person's right to privacy. Potential threats and violations of a person's privacy rights containing important personal information are increasingly prevalent. Almost unrestricted digital access activities pose a threat of violating one's right to privacy. Personal data containing important information may be accessed by the Data Controller and potentially disseminated freely and irresponsibly. The right to privacy is part of the human right to obtain protection for oneself based on Article 28 Letter G section (1) The 1945 Constitution of The Republic of Indonesia. The purpose of this study is to analyze the protection of the right to privacy of personal data. This research uses normative juridical research methods by exploring legal sources. With the passing of Law Number 27 of 2022, it is hoped that it will increase understanding of the right to privacy of personal data of every citizen to be able to maintain and be responsible for the right to privacy of personal data and become a legal umbrella for the Indonesian nation in upholding justice and providing legal protection in accordance with applicable regulations.

Personal data governance and privacy in digital reproductive, maternal, newborn, and child health initiatives in Palestine and Jordan: a mapping exercise.

There is a rapid increase in using digital technology for strengthening delivery of reproductive, maternal, newborn, and child health (RMNCH) services. Although digital health has potentially many benefits, utilizing it without taking into consideration the possible risks related to the security and privacy of patients' data, and consequently their rights, would yield negative consequences for potential beneficiaries. Mitigating these risks requires effective governance, especially in humanitarian and low-resourced settings. The issue of governing digital personal data in RMNCH services has to date been inadequately considered in the context of low-and-middle-income countries (LMICs). This paper aimed to understand the ecosystem of digital technology for RMNCH services in Palestine and Jordan, the levels of maturity of them, and the implementation challenges experienced, particularly concerning data governance and human rights. A mapping exercise was conducted to identify digital RMNCH initiatives in Palestine and Jordan and mapping relevant information from identified initiatives. Information was collected from several resources, including relevant available documents and personal communications with stakeholders. A total of 11 digital health initiatives in Palestine and 9 in Jordan were identified, including: 6 health information systems, 4 registries, 4 health surveillance systems, 3 websites, and 3 mobile-based applications. Most of these initiatives were fully developed and implemented. The initiatives collect patients' personal data, which are managed and controlled by the main owner of the initiative. Privacy policy was not available for many of the initiatives. Digital health is becoming a part of the health system in Palestine and Jordan, and there is an increasing use of digital technology in the field of RMNCH services in both countries, particularly expanding in recent years. This increase, however, is not accompanied by clear regulatory policies especially when it comes to privacy and security of personal data, and how this data is governed. Digital RMNCH initiatives have the potential to promote effective and equitable access to services, but stronger regulatory mechanisms are required to ensure the effective realization of this potential in practice.

People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design.

The use of digital technology amongst people living with a range of long-term health conditions to support self-management has increased dramatically. More recently, digital health technologies to share and exchange personal health data with others have been investigated. Sharing personal health data with others is not without its risks: sharing data creates threats to the privacy and security of personal data and plays a role in trust, adoption and continued use of digital health technology. Our work aims to inform the design of these digital health technologies by investigating the reported intentions of sharing health data with others, the associated user experiences when using these digital health technologies and the trust, identity, privacy and security (TIPS) considerations for designing digital health technologies that support the trusted sharing of personal health data to support the self-management of long-term health conditions. To address these aims, we conducted a scoping review, analysing over 12,000 papers in the area of digital health technologies. We conducted a reflexive thematic analysis of 17 papers that described digital health technologies that support sharing of personal health data, and extracted design implications that could enhance the future development of trusted, private and secure digital health technologies.