Privacy Research Articles

Merging Realities for Better Health: A Dive Into Virtual Healthcare

This paper delves into the transformative use of Virtual Reality in the public health domain, focusing on its psychological effects, the effectiveness of VR-based patient education interventions, and ethical considerations surrounding the use of personal data and privacy. Through the examination of peer-reviewed studies, the research found that VR offers innovative solutions on health education and mental health outcomes. The immersive digital environment of VR offers breakthroughs in delivering patient education and psychological interventions. As VR devices continuously collect vast amounts of sensitive user information to create unique experiences, the paper discusses the need for data protection and ethical guidelines to protect user privacy and gain consumer trust. However, it is important to address the significant ethical and social issues, and adverse reactions, such as cyber-sickness or escapism, to fully realize the benefits of Virtual Reality.

Analysis of Personal Privacy Leakage under the Background of Digital Twin

With the development and application of digital twin technology, the problem of privacy leakage is becoming increasingly prominent. Privacy leakage issues caused by data abuse and weak awareness have attracted high attention. In this regard, this paper analyzes the causes of privacy leakage in the digital twin, analyzes the difficulties of privacy protection under the background of the digital twin, and puts forward the “legal co-governance model.” Through multi-party cooperation, improving the system, and improving literacy, people can effectively protect private information, ensure the security application of technology, law, and ethics, and provide a guarantee for the sustainable development and social application of digital twin technology.

Cybersecurity and personal privacy: Protecting yourself in the digital age

The internet has revolutionized communication, work, and life, offering unprecedented opportunities and conveniences. However, as our dependence on the digital world grows, so do the risks to our personal information, privacy, and security. Cyberattacks, online harassment, and cyberstalking are no longer rare occurrences—they have become everyday threats that anyone can face. Understanding and implementing robust cybersecurity measures is essential for protecting yourself in this increasingly connected world. This article explores effective ways to safeguard against cyberattacks, respond to online harassment, prevent cyberstalking, and remove your personal information from websites that share it without your consent.

The Infringement of Deepfake Technology on Personal Privacy and Legal Protection: A Discussion Based on Article 1032 of the Civil Code

The rapid development of deepfake technology has infringed upon personal privacy in the form of the disclosure and misuse of personal information, infringement upon the peace of people's life, and damage to personal image and reputation. The disclosure and misuse of personal information should be analysed from the perspectives of personal use and commercial use of information data; infringement upon the peace of people's life should be examined from the perspectives of fraud and harassment, and malicious pursuit using deepfake technology; and damage to personal image and reputation should be discussed from the two levels of initial damage to personal image and reputation and aggravated damage to personal reputation after dissemination through the media. Article 1032 of the Civil Code clearly defines the scope of privacy protection as the peace of people's private life, private space, private activities and private information, as well as the infringement of privacy rights through means such as spying, intrusion, disclosure and publication, which reflects the legislative inertia to strengthen the protection of privacy and personal information. Article 1032 of the Civil Code, the Personal Information Protection Law, the Provisions on the Administration of Deep Synthesis of Internet-based Information Services, and other laws and regulations together form the current personal privacy protection system. However, existing laws do not provide specific explanations for key terms such as 'leakage', 'intrusion' and 'damage', which leads to uncertainty in the application of the law and makes it difficult to effectively protect personal privacy in actual operations. At the same time, there is a lack of clear definitions of the scenarios and specific standards of behaviour that the technology may involve, which has exacerbated public concerns about personal privacy security. Strengthening legal protection against deepfake technology requires refining legal concepts and drawing on knowledge from comparative law: 'leakage' can be considered the illegal acquisition and dissemination of unauthorised data and information, 'intrusion' can be defined as behaviour that disrupts the normal order of others and causes mental distress, and 'damage' can be understood as behaviour that causes negative evaluation or loss to the image, reputation or property of others. The legislative department should take the lead and cooperate with the Ministry of Industry and Information Technology in formulating specific standards for evaluating behaviour. The deterrent effect of the law should be strengthened by refining relevant legal provisions in the Civil Code and the Personal Information Protection Law and increasing the penalties for violations of the law. This will better balance technological development and personal privacy protection and improve China's personal privacy protection system.

A Privacy-Preserving Data Mining Through Comprehensive GNIPP Approach in Sensitive Data Sets

The quick growth of methods for analyzing data and the availability of easily available datasets have made it possible to build a thorough analytics model that can help with support decision-making. In the meantime, protecting personal privacy is crucial. A popular technique for medical evaluation and prediction, decision trees are easy to comprehend and interpret. However, the decision tree construction procedure may reveal personal information about an individual. By keeping the statistical properties intact and limiting the chance of privacy leaking within a reasonable bound, differential privacy offers a formal mathematical definition of privacy. To construct a boosting random forest that preserves privacy, we propose a Gaussian Noise Integrated Privacy Preservation (GNIPP) in this study. To address the issue of personal information breaches, we have designed a unique Gaussian distribution mechanism in GNIPP that enables the nodes with deeper depth to obtain more privacy during the decision tree construction process. We propose a comprehensive boosting technique based on the decision forest's prediction accuracy for assembling multiple decision trees into a forest. Furthermore, we propose an iterative technique to accelerate the assembly of decision trees. After all, we demonstrate through experimentation that the suggested GNIPP outperforms alternative algorithms on two real-world datasets.

A Study on User Privacy Information Sharing and Boundary Management on Social Media

This article aims to study and analyze issues related to user privacy information sharing and limitation management on social media. It examines the actions users take regarding information sharing in various situations and proposes effective privacy management measures to help users better secure their personal privacy. Based on survey data, the analysis makes two key assumptions: first, there is a negative correlation between users’ reception of privacy management policies and the frequency of experiencing privacy information leaks; second, there is a positive correlation between the reception of privacy management policies and users’ privacy management actions. The analysis reveals several findings: privacy information management practices are generally prevalent among social media users, users face significant challenges in managing privacy information, and most social media users have experienced personal privacy information leaks. These insights are crucial for understanding privacy management actions in social media environments and for developing effective privacy management policies.

Romance and Worries: AI Partners and Young Adults’ Emotional Relationships

With the rapid development of science and technology, AI artificial intelligence has been comprehensively developed and penetrated into every corner of life. With the emergence of updated human-computer interaction products, AI partners gradually enter people’s daily lives and becoming more and more popular among the younger demographics. Aiming to clarify the emotional relationship between young adults and their AI partners, this study takes adolescents as research subjects and uses online observation and literature research methods. Research has found that young adults’ emotional interactions with AI partners can have a dual impact. Using an AI partner wisely, they can gain positive psychological comfort, get rid of emotional loneliness, and improve social skills. Yet, over-indulgence in the world of AI partners can also negatively affect young adults’ growth, leading to disappointment in the real world and disclosure of their personal privacy. In order to maximize the positive effects of AI and prevent over dependence, this study suggests promoting the technological upgrading of the AI industry, emphasizing the guidance of family education, and strengthening legal regulation, so as to promote the scientific use of AI partners and social harmonious development together. Moreover, this research can make people further understand the field of AI emotional communication, let people have more tolerance for users, and make this way of communication further into people’s lives.

A privacy-preserving federated learning approach for airline upgrade optimization

PurposeA key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers. Design/methodology/approachFederated learning is a new confidential computing technique that allows companies to train a model cooperatively by exchanging model parameters instead of the actual raw data, which might include customers' privacy sensitive information. Using a case study of a Chinese airline company, this study demonstrated how FL-based upgrade models using multi-source data can be developed to improve the accuracy of predicting customers' willingness to pay for upgrades while preserving customers’ personal data privacy. FindingsComparing with traditional unilateral model using single-source data, the federated logistic regression and SecureBoost models demonstrate better model performance. This indicates that the proposed FL approach can enhance the accuracy of modeling airline passengers' willingness to pay for upgrade offers while preserving passengers’ data privacy. The findings also show that the FL-based models generally took longer running time than the traditional unilateral model due to the design of FL approach in ensuring data privacy. OriginalityThis study contributes to the literature of upgrade optimization by introducing the new FL approach for developing machining learning models to predict customers’ reaction to upgrade offers. Although we focus on the airline industry in our case study, the proposed FL approach can be applied to other industries with a similar issue of upgrade optimization such as hotels or cruise lines, and car rental.

Security Protection of Video-GIS Data Based on Data Encryption and Digital Watermarking

Abstract. Video-GIS data, a novel generation of multimedia produced through the integration of geographic videos and GIS, has evolved into an innovative form of geospatial data representation. As a kind of digital products, Video-GIS data is vulnerable to unauthorized replication, dissemination, and usage. The potential for large-scale leakage of sensitive or confidential information, including commercial, political, military, and personal privacy data, poses a significant threat to the rights of data producers and may even compromise national security. Given this context, the primary concern is ensuring the security protection of video data in an open and shared data environment. To address this issue, we propose a novel scheme for the security protection of Video-GIS data in this paper, which collaboratively utilizes digital watermarking and data encryption. In the proposed scheme, Video-GIS data is divided into two categories, i.e., general and confidential, based on the presence or absence of sensitive information within the image frames. Appropriate security measures were meticulously designed for each data type to ensure effective prevention and control. Experimental results indicate that this watermarking algorithm exerts minimal influence on the quality of the Video-GIS data, while simultaneously maintaining optimal invisibility and robustness. The employed audio denoising method is characterized by its low complexity, simplicity, and feasibility, thereby ensuring the security of audio signal storage and transmission. This approach effectively realizes the dual protection objective for Video-GIS data, audio, and video.

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.

Mobile apps for health surveillance: Balancing public health needs with the privacy of personal data

The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.

Privacy Policies in Public Libraries of the Gulf Cooperation Council (GCC) Countries: Open Doors, Closed Windows

In an era dominated by digital technologies and the exponential growth of data, safeguarding personal privacy is more crucial than ever. This research investigates the privacy policies of public libraries in the Gulf Cooperation Council (GCC) countries, aiming to understand the extent to which these policies protect patrons' privacy rights regarding openness and restrictions. The study employs a qualitative method using structured content analysis of policy documents available through the official websites of the selected public libraries. This study will report coding results to indicate how public libraries in GCC countries address patron and user privacy, including exemptions to established laws and authorities’ protection. This research aims to contribute to the broader discussion on the region's privacy rights and data protection. The findings of this study will provide insights into current privacy practices and identify areas for improvement. The implications of this research will support recommendations for policymakers and the Ministries of Culture in GCC countries.

Disclosing Personal Health Information to Emotional Human Doctors or Unemotional AI Doctors? Experimental Evidence Based on Privacy Calculus Theory

The commercialization of artificial intelligence (AI) in healthcare is accelerating, yet academic research on its users remains scarce. To what extent are they willing to disclose personal health privacy to AI doctors compared to traditional human doctors? What factors are shaping these decisions? The lack of user research has left these questions unanswered. This article, based on privacy calculus theory, conducted a multi-factorial between-subjects online experiment (N = 582) with a 2 (medical provider: AI vs. human) × 2 (emotional support: low vs. high) × 2 (information sensitivity: low vs. high) design. The results indicated that AI doctors lead participants to perceive both lower health benefits and privacy risks. Emotional support is not always beneficial. On one hand, high emotional support can provide patients with more health benefits, but on the other hand, it also poses higher levels of privacy risks. Additionally, high emotional support responses from AI doctors could enhance patients’ health benefits, trust, and willingness to disclose health privacy, while the opposite was observed for human doctors.

Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure

BackgroundPersonal health data is crucial for effective medical care, personalized treatment, and health monitoring. It enables accurate diagnosis, efficient treatment plans, and informed healthcare decisions. Personal health data should be protected to ensure patient privacy, prevent misuse or unauthorized access, and maintain trust in healthcare systems, thereby safeguarding individuals’ sensitive information from potential harm or exploitation. Therefore, this study aimed to investigate whether perceived risk and perceived benefits have mediating roles in the relationships among individuals’ personal health information disclosure behaviour, perceived control, and privacy concerns.MethodThe population of the study consisted of individuals living in the provinces of Izmir, Konya and Adana. The sample of the study consisted of individuals who were reached through a convenience sampling method. The scales for privacy concerns, perceived control, perceived risk, perceived benefits and information disclosure behaviour were used in the study. Cronbach’s alpha and the AVE were calculated, and a confirmatory factor analysis was performed. A path analysis was performed using the structural equation model to test the hypotheses.ResultsThe analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. However, perceived risk and perceived benefit did not mediate this relationship. Additionally, a significant positive relationship was found between individuals’ behaviour of disclosing their perceived control and personal health data, with perceived risk and benefits playing a mediating role in this relationship.ConclusionThe study concluded that as individuals’ concerns about sharing personal health data increase, they are less likely to share these data. It was also found that perceived risk and perceived benefit mediate this relationship. Additionally, higher perceived risk intensifies privacy concerns, further discouraging data sharing, while perceived benefits can mitigate these concerns, promoting greater willingness to disclose health information.

Consumer privacy decision-making in digital era: an integrative review and taxonomy

PurposeIn the digital age, personalized services and accurate recommendations enhance the customer experience and streamline shopping. However, increasing concerns about personal privacy have led to resistance from consumers, necessitating a balance between providing high-quality online services and safeguarding personal data. The aim of this paper is to offer a comprehensive review of the fragmented literature on consumer privacy decision-making and to identify key issues worth exploring in future research.Design/methodology/approachAlthough previous studies have analyzed the antecedents and outcomes of privacy decisions, they have often been conducted in a fragmented manner. There remains a lack of a holistic understanding of the factors influencing privacy decisions, including their boundaries. Therefore, we build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We attempt to dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, ultimately providing a comprehensive framework for understanding these dynamics.FindingsBased on the Theory of Planned Behavior, we analyze the entire process of consumer privacy decision-making in terms of antecedent, moderating and outcome variables. The results indicate that consumer privacy decision-making is not an isolated behavior or a single choice but a complex, multi-level dynamic process. The factors influencing consumer privacy decisions primarily encompass five aspects: individual characteristics, information, organization, platform and interaction management, leading to various outcomes in both behavioral and perceptual dimensions. Furthermore, the process is constrained by multiple moderating variables, such as information sensitivity, platform knowledge and prior experience.Originality/valueWe build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, aiming to provide a comprehensive framework for understanding these processes.

Input panel for handwriting detection based on triboelectric nanogenerator

Handwritten signatures are widely used in our daily lives, which focuses on how to obtain handwritten information comprehensively and effectively. Triboelectric nanogenerators can sensitively sense externally applied trigger signals and can be used to collect user handwriting signals and related features for handwriting input character detection and user identification. In this paper, an intelligent handwriting input panel based on triboelectric nanogenerator is proposed, which, combined with deep learning algorithms, achieves an accuracy of 99.32%, 98.96%, 99.14%, and 99.53% for recognizing handwritten signals in Arabic numerals, English words, Chinese characters, and different users, respectively. It is also possible to use different encoding methods for encrypted communication to transmit signals on the proposed handwriting input panel, demonstrating its potential applications. The results show that the smart handwritten input panel has great potential for personal handwritten signature recognition, privacy information, and human–computer interaction.

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

Extend UTAUT2 Model to Analyze User Behavior of China Construction Bank Mobile App

The outbreak of COVID-19 has been accelerating the digital transformation of financial institutions all over the world. Mobile banking APPs become an important channel to provide financial services in the Internet and Big Data era. Factors that restrict its development have attracted the close attention of scholars and practitioners around the world. Different from a general APP software, mobile banking APPs naturally have some financial attributes which set high requirements for personal privacy protection and other security issues. For this reason, this paper extends the original UTAUT2 model by incorporating a new constructor: perceived risks. The study carefully selects China Construction Bank’s mobile banking app (short as “the CCB APP”), as she is the biggest bank in China and has good novelty and representativeness in financial technology. This paper constructs an extended UTAUT2-based model using questionnaires and structural equation modeling the user behavior of CCB APP and tests the influencing factors of the proposed model through empirical analysis. Similar to other studies using the UTAUT2 model, the findings of this paper indicate that performance expectations, effort expectations, social influence, facilitators, and habits, all have a positive impact on CCB APP users’ intentions. Their intention to use was a mediating variable of user behavior, while facilitators directly influenced CCB users’ behavior. However, the study finds that perceived risk negatively affects the CCB users’ intention while hedonic motivation and price value do not significantly affect users’ decision-making process. Based on these findings, the misdirection should to be avoided in regard to financial APP development by financial institutions is discussed and the future research direction concerning mobile banking app adoption is illustrated.

The Economic Impact of AI Technology – Take Claude and GPT-4 As Examples

With the rapid development of science and technology, artificial intelligence (AI) technology has become the core field of global economic competition. Especially in recent years, the emergence of advanced AI models such as Claude and GPT-4 has not only led to a new round of technological revolution, but also had a profound and extensive impact on the global economy. The competition in these AI technologies is reshaping the economic pattern of various industries, creating new growth drivers, but also bringing many challenges and uncertainties. In the manufacturing sector, the application of AI technology has greatly improved production efficiency and quality, but it also requires companies to constantly update equipment and train employees to adapt to new production models. In the service industry, the application of AI customer service, intelligent recommendation and other technologies has improved the customer experience, but also put forward higher requirements for the skills and quality of employees. In addition, the wide application of AI technology also poses new challenges to data security, privacy protection and other aspects. How to enjoy the convenience brought by technology while ensuring data security and personal privacy has become an urgent problem to be solved. Therefore, in the face of the economic changes brought about by AI technology competition, policy makers, entrepreneurs and researchers need to conduct in-depth research and discussion, formulate reasonable policies and strategies, to better respond to challenges, seize opportunities, and promote the sustainable and healthy development of the economy.

Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People

Lifelong learning for all has gradually become a norm in developing education policies in various countries. Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People attempts to analyze and address the issues above from the data continuous development and improvement perspective. These issues include Unequal distribution of resources, Rapid technological upgrading, Lack of appropriate platforms, and An urgent need for personalized learning, Data security and privacy. China has made some progress in promoting the construction of a digitally empowered learning society while facing some challenges and problems. The solutions include:●Strengthen the overall planning of the platform.●Integration of crucial application scenarios.●Implementing data governance to build a lifelong learning data warehouse.●Using platforms to strengthen the application of learning outcomes.●Construction of platform network security guarantee system.