Types Of Personal Information Research Articles

안면인식기술과 개인정보보호

Face recognition technology is a method that utilizes thermal infrared photography, three-dimensional measurement, and skeletal analysis to scan, store, and recognize facial shapes or thermal images. It verifies identity by comparing facial images captured by cameras with those in a stored photo database. This technology is extensively applied in various sectors, such as identity verification and access control, due to its high accuracy and quick identification capabilities. The advancement of facial recognition technology, coupled with CCTV, the Internet of Things (IoT), and widespread network connections, is materializing dystopian fears concerning omnipresent surveillance and the ensuing erosion of privacy. Additionally, biased facial recognition technology may serve as a tool for discrimination, infringing upon the rights and freedoms of minorities and other marginalized groups. In 2023, the National Human Rights Commission of The Republic of Korea issued a statement regarding human rights violations facilitated by facial recognition technology and urged the Speaker of the National Assembly and the Prime Minister to actively respond. The statement highlighted that direct observation of individuals' faces in public spaces by the state could limit citizens' freedom of movement and lead to unwarranted surveillance. Given the unique nature of biometric information, such as facial recognition technology, the repercussions of data breaches and misuse are more severe compared to other types of personal information. Specifically, real-time facial recognition technology that targets an unspecified number of individuals in public spaces without their consent may infringe upon the right to privacy and informational self-determination, as well as other fundamental rights including privacy, freedom of assembly, and freedom of association. Furthermore, real-time facial recognition security systems pose challenges in terms of privacy protection and invasion. Hence, the security of collected facial data, defenses against hacking threats, and the secure management of facial databases require thorough examination. This study explores the legal framework and international legislative trends related to biometric information, including facial recognition technology, to identify measures for protecting personal information and ensuring the safe use of this technology.

Instruments and indicators for assessing organisational food environments: a scoping review protocol

IntroductionMany studies have explored the food environment to characterise it and understand its role in food practices. Assessment of the organisational food environment can contribute to the development of more...

Motivations to learn genomic information are not exceptional: Lessons from behavioral science.

Whether to undergo genome sequencing in a clinical or research context is generally a voluntary choice. Individuals are often motivated to learn genomic information even when clinical utility-the possibility that the test could inform medical recommendations or health outcomes-is low or absent. Motivations to seek one's genomic information can be cognitive, affective, social, or mixed (e.g., cognitive and affective) in nature. These motivations are based on the perceived value of the information, specifically, its clinical utility and personal utility. We suggest that motivations to learn genomic information are no different from motivations to learn other types of personal information, including one's health status and disease risk. Here, we review behavioral science relevant to motivations that may drive engagement with genome sequencing, both in the presence of varying degrees of clinical utility and in the absence of clinical utility. Specifically, we elucidate 10 motivations that are expected to underlie decisions to undergo genome sequencing. Recognizing these motivations to learn genomic information will guide future research and ultimately help clinicians to facilitate informed decision making among individuals as genome sequencing becomes increasingly available.

Monetary Compensation and Private Information Sharing in Augmented Reality Applications

This research studied people’s responses to requests that ask for accessing their personal information when using augmented reality (AR) technology. AR is a new technology that superimposes digital information onto the real world, creating a unique user experience. As such, AR is often associated with the collection and use of personal information, which may lead to significant privacy concerns. To investigate these potential concerns, we adopted an experimental approach and examined people’s actual responses to real-world requests for various types of personal information while using a designated AR application on their personal smartphones. Our results indicate that the majority (57%) of people are willing to share sensitive personal information with an unknown third party without any compensation other than using the application. Moreover, there is variability in the individuals’ willingness to allow access to various kinds of personal information. For example, while 75% of participants were open to granting access to their microphone, only 35% of participants agreed to allow access to their contacts. Lastly, monetary compensation is linked with an increased willingness to share personal information. When no compensation was offered, only 35% of the participants agreed to grant access to their contacts, but when a low compensation was offered, 57.5% of the participants agreed. These findings combine to suggest several practical implications for the development and distribution of AR technologies.

Research on Legal Protection of Minors' Data in Digital Age

Minors are a special group with immature heart and in the growing stage, whose rights and interests are more vulnerable to the infringement of the Internet information world. For minors, it is necessary to take special consideration and design in the protection rules of the law. Different types of personal information will be distinguished, combined with the specific application situation, to further distinguish the age of consent matters, to maximize the balance between the relatively independent personality of minors and the protection of personal information conflict. The protection of minors' personal information is not the responsibility of only one party.

Does personalization of officeholders undermine the legitimacy of the office? On perceptions of objectivity in legal decisionmaking

AbstractPublic legitimation of legal decisionmaking can be promoted through various strategies. We examine strategies of legitimation that are premised on personalizing the public image of legal agents. A personalized public administration emphasizes individual decisionmakers and seeks legitimacy through familiarity with the character, identity, and virtues of individual agents, whereas a non‐personalized public administration projects an ethos of technocratic decisionmaking, seeking legitimacy through institutional objectivity and impartiality. We conducted an experiment to examine the efficacy of personalization strategies in the context of a politically charged legal affair: the criminal cases involving the prime minister of Israel, Benjamin Netanyahu. We focus on people's perceived objectivity of the office of the Israeli attorney general (AG), given exposure (vs. no exposure) to different types of personal information about the AG, and while manipulating the salience of contrasting decisions concerning Netanyahu (indicting him on several counts of corruption versus exculpating him in others). We find that exposure to personal information about the AG decreased the perceived objectivity of his office, compared to no exposure to personal information, regardless of the type of information, decision salience, and respondents' political leanings. Our findings, therefore, support the legitimating potential of the non‐personalization of decisionmakers, and show that it pertains to people positioned as both “losers” and “winners” with regard the political impact of the decision. The study further reflects the capacity of nonabstract real‐world, real‐time, analyses to shed light on the drivers of public trust in legal decisionmaking in politically polarized contexts—an issue of pertinence in many contemporary democracies.

Statistical Feature-Based Personal Information Detection in Mobile Network Traffic

With the popularity of smartphones, mobile applications (apps) have penetrated the daily life of people. Although apps provide rich functionalities, they also access a large amount of personal information simultaneously. As a result, privacy concerns are raised. To understand what personal information the apps collect, many solutions are presented to detect privacy leaks in apps. Recently, the traffic monitoring-based privacy leak detection method has shown promising performance and strong scalability. However, it still has some shortcomings. Firstly, it suffers from detecting the leakage of personal information with obfuscation. Secondly, it cannot discover the privacy leaks of undefined type. Aiming at solving the above problems, a new personal information detection method based on traffic monitoring is proposed in this paper. In this paper, statistical features are designed to depict the occurrence patterns of personal information in the traffic, including local patterns and global patterns. Then a detector is trained based on machine-learning algorithms to discover potential personal information with similar patterns. Since the statistical features are independent of the value and the type of personal information, the trained detector is capable of identifying various types of privacy leaks and obfuscated privacy leaks. As far as we know, this is the first work that detects personal information based on statistical features. Finally, the experimental results show that the proposed method could achieve better performance than the state-of-the-art.

Legal Regulation and Path Improvement of Face Recognition Information Protection from the Perspective of Comparative Law

With the increasing popularity of artificial intelligence technology, face recognition technology has shown explosive growth, but high speed and risk coexist. In recent years, the number of cases caused by face recognition has been increasing at home and abroad. The civil law system has paid attention to the protection of face information, a particular type of personal information. However, whether the existing law can fully deal with the face recognition technology under the new technology development situation and whether it can effectively protect it still needs further demonstration and analysis. From many face recognition infringement cases, we can see that the abuse of face recognition technology and the infringement of face information have caused severe violations of personal rights and even endanger public security. With the introduction of the Personal Information Protection Law and Civil Code, information subjects obligations and legal consequences are stipulated, which better meets the practical needs of face recognition information protection. However, there are still some problems in practice, such as imperfect standards of face recognition information obligations, unclear definition of the 'safety, legality, legitimacy and necessity' principle, poor protection of sensitive personal information, complex relief of data space infringement, and unclear connection with criminal law. Therefore, based on the analysis of extraterritorial experience and domestic legislation and judicial practice, we can further improve the problems above and optimize the path of legal protection.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research.

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

Ethics in the use of geospatial information in the Americas

During the last decade, the use of geospatial information in decision-making has reached an especially important role, which is why its production has increased considerably. The particularities that individual geospatial information presents with respect to other types of personal information, such as the linking of sensitive information, has begun to generate concerns regarding the privacy of information and individual surveillance, being increasingly difficult to guarantee and ensure its protection, while other ethical problems and risks related to its use arise. For this reason, the development and implementation of principles and guidelines of conduct has become an increasingly necessary component to address the ethical challenges associated with the use of geospatial information, in order to achieve a greater perception of its benefits and less perception of their risks. However, at present, there is no common set of global ethical principles to guide their responsible use. Therefore, to achieve this, it is important to know the advances at the global and regional level in the generation and implementation of principles and guidelines related to the ethical use of geospatial in various cultural and political contexts. In this way, this article aims to present, for the first time, the methodology developed, and the results obtained for the evaluation of the current state of the design and implementation of strategies aimed at achieving the ethical use of geospatial information in the region of the Americas. The results indicate that there is a need to strengthen the issue of the ethical use of geospatial data in the region, since, although it is beginning to be included in the government agenda, there is still little awareness among generators and users of geospatial information about the risks and consequences of its misuse.

Genetic discrimination and protection of personal genetic data: Adapting legal standards to advances in genetics

Genetics has advanced to the point where genetic data on an individual could mark them as predisposed to hereditary illness or unsuitable for certain kinds of jobs. There is widespread apprehension that workers with ‘problematic’ genetics will be singled out by employers and insurance companies for treatment as second-class citizens with restrictions placed on their rights. The article takes up the legal issues involved in defining the concept of genetic data, in regulating genetic information as a type of personal information, and in applying genetic antidiscrimination laws in various countries. Legal restraints on genetic information must be more extensive than on other personal information about a worker because genetic data has implications for their biological relatives. Protection against genetic discrimination must therefore begin with barring employers from collecting genetic information on workers unless it is necessary to prevent hazards to people’s lives or health.

Mining the Data: Exploring Rural Patients’ Attitudes about the Use of Their Personal Information in Research

Background This study examines rural patients’ perceived importance of knowing or being consulted about researchers’ access and use of their personal data (identifiable and de-identified health information, and identifiable and de-identified non-health information) across five scenarios. This study also examines their views on stewardship or governance of their personal information by researchers in their healthcare systems. Methods We conducted a survey by mail. Data were analyzed using descriptive statistics. Multivariable regression analyses were conducted across each scenario and type of personal data with the same variables included in each model. Results The majority of participants said it was “very important/absolutely essential” to know the purpose of the study, to be asked every time, and to know the policies governing researcher access and use of their identifiable health information. Just over two-thirds of respondents thought it “very important/absolutely essential” to know who serves on the data governance committee and to have a community member serve. Distrust in healthcare organizations was positively correlated with the scenarios while willingness to give permission to donate leftover biological specimens was negatively correlated. Conclusion Our study findings indicate that the type of personal information being accessed and used generally matters to 1,407 patients living in rural Pennsylvania. We also demonstrate that knowing their healthcare organizations’ governance policies and practices for managing their personal data is important to many rural Pennsylvania patients. Biomedical researchers need to recognize and attend to those differences as much as possible in order to expand opportunities for and participation in research by residents of these rural communities. Supplemental data for this article is available online at

How power and personality trait of others affect impression: Evidence from event-related potentials

It is well known that the personality traits and power associated with peoples’ impressions are represented in the memory system. However, there is scant evidence that these two types of personal information have an interaction effect on face impression, and in which processing stage it happens. The purpose of this study was to provide evidence for the temporal processing of personality traits and power in the retrieval of face impressions. We recorded event-related potentials when participants recognized faces. Compared to the low-power condition a clear positive-going ERP response for the high-power condition was found in the faces with positive traits, but the pattern reversed in the faces with negative traits in the time windows of 100–200 ms (N170), 250–350 ms (EPN), and 350–450 ms (N400). These findings illustrate for the first time the effect of personality traits in the encoding and recognition of personal faces could be moderated by power, directly suggesting negative traits for the powerless while positive traits for the powerful.

Factors affecting the perception of students at the University of Social Sciences and Humanities, Vietnam National University Ho Chi Minh City, on the issue of personal information security

Thanks to the trend of digital transformation and the promotion of people’s legitimate rights and interests, personal information security has grown in importance in the society. Against such context, everyone, especially students, who are a valuable human resource in the age of science and technology, should be aware of the issues related to personal information security. The purpose of this study is to identify factors that influence students’ perceptions at the University of Social Sciences and Humanities, Vietnam National University Ho Chi Minh City, on personal information security. Data were gathered through a questionnaire survey of 392 students and interviews with four students from various faculties and academic years at the University of Social Sciences and Humanities, Vietnam National University Ho Chi Minh City. The study identified 12 factors that influence students’ perceptions of personal information security namely (1) information technology skills, (2) personality traits, (3) attitude, (4) experience, (5) knowledge, (6) work environment, (7) friends, (8) social networks, (9) habits, (10) environment life, (11) education level, and (12) types of personal information in possession. From the findings, some recommendations regarding students’ perception of personal information security are presented.

Artificial Intelligence Is Safer for My Privacy: Interplay Between Types of Personal Information and Agents on Perceived Privacy Risk and Concerns

The development of novel technologies has increasingly complicated the issue of users' privacy. This study aimed to investigate the impact that the types of information as well as agents have on perceptions of privacy risks and concerns. The results showed that participants perceived privacy risk and were most concerned when they were asked to provide digital life information, followed by being asked for digital footprint information, and even more so when they were asked to provide this information to a human agent instead of an artificial intelligence agent. However, there were no differences in the perception of privacy risks and concerns with respect to self-expression and demographic information. The implications of these findings are also discussed.

Sharing Personal Information is Discounted as a Function of Social Distance

Social discounting researchers have repeatedly shown that individuals discount sharing the amount of a monetary reward as a function of social distance, and that increasing the available monetary reward decreases sharing. However, no previous study has tested whether sharing nonmonetary commodities are discounted as a function of social distance. The current study tested whether sharing personal information would be discounted similarly to monetary rewards, as well as whether a magnitude effect occurred at a relatively small magnitude difference with 96 university students. A within-participant procedure showed that sharing personal information was discounted as a function of social distance, albeit with a steeper discounting rate relative to both monetary reward magnitudes. However, there was no significant association between personal information discounting rates and monetary discounting rates at either magnitude, suggesting that participants treated each commodity differently (i.e., commodity effect). Replicating previous non-U.S. samples, discounting rates for both monetary reward magnitudes were significantly positively associated with each other and showed a significant magnitude effect, with participants showing significantly steeper discounting rates for the relatively larger monetary rewards. The results for sharing personal information are important because many scams now target personal information in addition to money. Future research should examine what type of personal information is most likely to be shared as a function of social distance, and whether those participants who choose to share more personal information also are at greater risk for scams targeting personal information.

Metadata based need-to-know view in large-scale video surveillance systems

Large-scale video surveillance systems are increasingly seen as the answer to problems concerning public safety, law enforcement, and situational awareness in public places. However, the unauthorized use of personal information derived from video data can be harmful. To preserve privacy, it is important to understand what type of personal information is contained in video surveillance data, and how much of that information is essential for an observer to achieve her authorized purpose. The purpose of the observer is described in terms similar to the information extracted by video surveillance systems, so they can be compared. This helps identify what type of information is better suited to control the flow of information to multiple observers, without compromising the privacy of the individuals. This paper presents a privacy-aware and need-to-know access control framework built on fine-grained data properties, extracted from surveillance data, which must conform to the explicitly defined purpose of the observers.

Self-Controllable Mobile App Protection Scheme Based on Binary Code Splitting

Mobile apps are booming with the expansion of mobile devices such as smartphones, tablet PCs, smartwatches, and IoT devices. As the capabilities of mobile apps and the types of personal information required to run apps have diversified, the need for increased security has grown. In particular, Android apps are vulnerable to repackaging attacks, so various code protection techniques such as obfuscation and packing have been applied. However, apps protected with these techniques can also be disabled with static and dynamic analyses. In recent years, instead of using such application level protection techniques, a number of approaches have been adopted to monitor the behavior of apps at the platform level. However, in these cases, not only incompatibility of system software due to platform modification, but also self-control functionality cannot be provided at the user level and is very inconvenient. Therefore, in this paper we propose an app protection scheme that can split a part of the app code, store it in a separate IoT device, and self-control the split code through the partial app. In the proposed scheme, the partial app is executed only when it matches the split code stored in the IoT device. It does not require complicated encryption techniques to protect the code like the existing schemes. It also provides solutions to the parameter dependency and register reallocation issues that must be considered when implementing the proposed code splitting scheme. Finally, we present and analyze the results of experimenting the proposed scheme on real devices.

Information Type Influences Students’ Perceptions of Faculty Self-Disclosures

Background: Faculty might disclose personal information in order to illustrate a concept related to course material. However, the impact of self-disclosing potentially personal information (e.g., medical or mental health) is unclear. Objective: The purpose of this study was to determine how students perceive professors who self-disclose personal information during lectures, given that some conditions may be viewed negatively. Method: Introductory psychology students ( N = 137) completed an online survey in which they rated female professors in vignettes describing course lectures. Each lecture was one of the following topics: bipolar disorder, opioid addiction, breast cancer, brain tumor, and animal learning (control). At the end of each vignette, the professor self-disclosed personal information related to that lecture. Results: Overall, students perceived professors who disclosed physical (breast cancer and brain tumor) conditions more positively compared to psychological (bipolar and opioid addiction) conditions. Highest approval ratings were given to the professor with breast cancer. Conclusion: Students’ perceptions of self-disclosure depended on the type of personal information, providing a springboard for future studies on this topic. Teaching Implications: Given the different levels of approval for each professor, we cannot make a blanket recommendation as to whether personal information should be shared in the classroom.

What Personal Information Can a Consumer Facial Image Reveal? Implications for Marketing ROI and Consumer Privacy

Massive availability of individuals’ facial images through online uploads and CCTV surveillance, combined with lacking regulation, presents potential for companies to obtain richer consumer data -- at the risk of privacy violations through exposure of personal information in such images. Building on rich survey data and supermarket video and receipt information, we provide systematic evaluation of relative predictability of different types of personal information -- through deep learning on facial images. We find facial images provide versatile statistical signals on individual's behaviors, preferences, character, personality, and beliefs. A large part of such predictive power is attributable to basic demographics extracted from the face. However, image artifacts, observable facial features, and deep image features extracted by a neural net all contribute to prediction accuracy beyond demographics. Using empirical evidence, a decision theory proof, and a simulation study, we argue such incremental information can meaningfully increase return on investment (ROI) when used for targeting. The limited accuracy of predictions from facial images attenuates the privacy risks to some degree, however, the variety of signals from a facial image is surprising. Prejudice caused by human decision makers' exposure to such statistical information is a substantial concern. We hope our results and the novel data we release with this work inspire research in the emerging area of facial analysis.