Personal Privacy Issues Research Articles

Personal Information Protection in Government Data Openness Using Decision Tree Model

To address the issues of personal privacy and information security in the current digital age, this study first collects and analyzes relevant government data openness policies and personal information protection laws and regulations to understand the current policy and legal environment. Secondly, a complexity pruning decision tree model is constructed, which can identify and evaluate potential personal information protection risks in government data openness. Using the Singapore government open dataset, this decision tree model is applied for empirical analysis, and its accuracy and effectiveness are evaluated. The research results demonstrate that the complexity pruning decision tree model performs well in terms of accuracy, recall rate, F1 score, and the area under the ROC Curve (AUC). The model achieves an accuracy of 0.85 on the training and 0.8 on the test sets, indicating its high performance in personal information protection in government data openness.

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation.

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification model based on transfer learning. Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power. In order to alleviate the problem of model overfitting caused by insufficient data set, transfer learning is used to train the model. The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network, and then retrain the model based on the CT image data set provided by Kaggle. Experimental results on a computer equipped only with the Central Processing Unit (CPU) show that it consumes only 1.06 s on average to diagnose a chest CT image. Compared to other lightweight models, the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters, which can be easily applied to computers without GPU acceleration. Code:github.com/ZhouJie-520/paper-codes.

Personal Privacy Data Protection in Location Recommendation System

At present, there are many location-based recommendation algorithms and systems, including location calculation, route calculation, and so on. However, in the general information data publishing, the privacy issues in the published data have not been fully paid attention to and protected. The purpose of this article is to investigate the effectiveness of personal privacy data protection in location recommendation systems. This paper first introduces the basis and importance of research on data security and secrecy, analyses personal privacy issues in data publishing in the era of big data, summarizes the research status in the field of security and secrecy at home and abroad, and introduces the process of data security and the role of users in it. Then, some classic privacy security modules in this field are introduced, and the privacy of data storage security concepts in the current situation mentioned in this paper is analyzed. A geographic location-based privacy protection scheme in mobile cloud is proposed. Privacy analysis, sensitive attribute generalization information analysis, route synthesis analysis and related experiments are performed on the location recommendation system. The experimental results show that the scheme proposed in this paper is more secure and has less loss of data availability.

Foundations for a decolonial big data psychology

AbstractThe accelerated datafication of social life has led to increased interest in advancing the use of big data in psychology. However, prevailing methodological preoccupations in big data psychology elide foundational issues which amplify the problems of modernity/coloniality, including the suppression of marginalized psychologies and the widened gap between researchers and their subjects. We propose decolonial foundations for utilizing big data in psychological scholarship. We move beyond prevailing technical concerns with data processing, to engage situated practices of knowledge production. The ontological object of computing shifts from a universal psychology of numerous individuals, to a localized psychology of collectives. Epistemological priorities shift from context‐free definition and control, to prioritizing temporal responsiveness and linguistic fidelity to emergent psychological phenomena. Ethical guidelines extend beyond issues of personal privacy, and address sociopolitical concerns in fragile democracies. Finally, reflexivities from the margins account for unequal systems that situate psychologists, big data, and the phenomena they study within the plurality of Global Souths. Two Philippine case studies operationalize these principles through localized data collection practices, participatory algorithmic design, interrogation of bias, and wrestling with state encroachments. We conclude with decolonial directions for advancing equitable practices of knowledge production within the computational and psychological sciences.

ASHP Foundation Pharmacy Forecast 2021: Strategic Planning Advice for Pharmacy Departments in Hospitals and Health Systems.

Joseph T. DiPiro, PharmD, FCCP, FAAAS, Dean, School of Pharmacy, Virginia Commonwealth University, Richmond, VA Address correspondence to Dr. DiPiro (ude.ucv@oripidtj). An underlying assumption supporting the need for the Pharmacy Forecast is that many factors influencing our profession and pharmacy services are not directly under our control, yet we can take actions that enhance the likelihood of favorable outcomes within this environment. Those influencing factors may be as a specific state or national policy or regulation, or as nebulous as the trend toward globalization (or anti-globalization). Other than the COVID-19 pandemic, the influencing factors such as the prominence of “big data,” issues of personal privacy, financing and health access are not new and have emerged over time. Within that context, then, we have greatest control over the scope of our pharmacy enterprise and the workforce within that enterprise, and some control over those factors where we can advocate to the decision makers (such as health-system administrators, legislators, and government agency officials). The perspective gained from reading the 2021 Pharmacy Forecast is most effectively used within the process of strategic planning as part of environmental scanning or when identifying strengths, weaknesses, opportunities, or threats (SWOT). In addition, the recommendations provided below can be part of the institution’s strategic planning action steps. FORECAST METHODS The methods used to develop the 2021 Pharmacy Forecast were similar to those used in the previous editions, drawing on concepts described in James Surowiecki’s book The Wisdom of Crowds.1 According to Surowiecki, the collective opinions of “wise crowds”—groups of diverse individuals in which each participant’s input is provided independently, drawing from their own locally informed points of view—can be more informative than the opinion of any individual participant. This process is particularly valuable when addressing phenomena that are not well suited to quantitative predictive methods. A critical requirement for successfully creating crowd-based knowledge is establishing a systematic method of combining individual beliefs into a collective opinion—the Pharmacy Forecast uses a survey of carefully selected pharmacy leaders to derive our environmental scan. The 2021 Pharmacy Forecast Advisory Committee (see membership list in the Foreword) began the development of survey questions by contributing lists of issues and concerns they believed will influence health-system pharmacy in the coming 5 years. That list was then expanded and refined through an iterative process, resulting in a final set of 7 themes, each with 6 focused topics on which the survey was built. Each of 42 survey items was written to explore the selected topics and was pilot tested to ensure clarity and face validity. As in the past, Pharmacy Forecast survey respondents—the Forecast Panelists (FPs)—were selected by ASHP staff after nomination by the leaders of the ASHP sections. Nominations were limited to individuals known to have expertise in health-system pharmacy and knowledge of trends and new developments in the field. The size of and representation within the Forecast Panel were intended to capture opinions from a wide range of pharmacy leaders. The Pharmacy Forecast survey instructed FPs to read each of the 42 scenarios represented in survey items and consider the likelihood of those scenarios occurring in the next 5 years. They were asked to base their response on their firsthand knowledge of current conditions in their region, not on their understanding of national circumstances. The panel was carefully balanced across the census regions of the United States to reflect a representative national picture. They were asked to provide a top-of-mind response regarding the likelihood of those conditions being very likely, somewhat likely, somewhat unlikely, or very unlikely to occur. This year we chose to present (in related articles in this issue of AJHP) additional insights on Pharmacy Forecast topics in the light of major societal factors, the U.S. presidential election, the COVID-19 pandemic, and racial equity and social justice within our country. William Zellmer was invited to reflect on the developing political environment after our national election and how it could impact healthcare.2 Suzanne Shea was invited to address the effects of the COVID-19 pandemic on selected topics within the Pharmacy Forecast survey.3 And Bruce Scott was invited to address the intersection of racial equity and healthcare.4

Tinjauan Ancaman dan Solusi Keamanan pada Teknologi Internet of Things

Internet of Things (IoT) adalah teknologi yang sangat populer akhir-akhir ini di seluruh dunia, yang membantu meng-hubungkan objek-objek seperti sensor, kendaraan, instrumen rumah sakit, industri, dan peralatan rumah tangga melalui internet. Perangkat pintar yang ringkas merupakan bagian penting dari IoT. Perangkat ini beragam dalam penggunaan, ukuran, sumber daya komputasi, dan memori. Namun, pengintegrasian peranti-peranti cerdas ini ke dalam jaringan internet memunculkan berbagai tantangan keamanan karena mayoritas teknologi internet dan protokol komunikasi tidak dirancang secara khusus untuk mendukung peranti IoT. Selain itu, komersialisasi IoT telah menyebabkan masalah keamanan, termasuk masalah privasi, ancaman serangan dunia maya, dan kejahatan terorganisasi. Untuk memberikan pedoman bagi pihak-pihak yang ingin mengembangkan keamanan IoT dan berkontribusi pada peningkatannya, survei ini berupaya memberikan daftar kerentanan dan penanggulangan secara komprehensif terhadap ancaman-ancaman tadi. Untuk mencapai tujuan ini, pertama-tama dipaparkan tiga model referensi IoT yang dikenal luas dan mendefinisikan keamanan dalam konteks IoT. Kedua, dibahas kemungkinan penerapan IoT pada berbagai bidang dan potensi serta motivasi para penyerang yang menargetkan teknologi IoT ini. Ketiga, dilakukan identifikasi dan klasifikasi berbagai serangan dan ancaman IoT. Keempat, dideskripsikan metode penanggulangan terhadap serangan IoT. Pada bagian akhir, diperkenalkan dua tantangan keamanan IoT yang muncul yang

Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion

Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era [10][20]. To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.

A Study of Optimizing Search Engine Results Through User Interaction

In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user’s operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users’ search and click records is a huge storage space in today’s Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results.

Access Control Based on Ciphertext Attribute Authentication and Threshold Policy for the Internet of Things.

The development of the Internet of Things has led to great development of data sharing and data interaction, which has made security and privacy more and more a concern for users. How to ensure the safe sharing of data, avoid the leakage of sensitive information, and protect the privacy of users is a serious challenge. Access control is an important issue to ensure the trust of the Internet of Things. This paper proposes an access control scheme based on ciphertext attribute authentication and threshold policy, which uses the identity authentication of hidden attributes and divides the user’s permission grade by setting the threshold function with the user’s attributes. Users obtain different permission grades according to attribute authentication and access data of different sensitivity grades to achieve fine-grained, flexible and secure access to data in the cloud server while protecting personal privacy issues. In addition, when the resource is acquired, the identity and permission joint authentication method is adopted to avoid the collusion attack of the illegal member, which makes the resource access control more secure.

On Being Known: God and the Private-I

Given recent discussions of personal privacy, or more particularly, its invasion via the internet, it is not surprising to find the issue of personal privacy emerging regarding God’s relation to our private lives. Two different and opposing views of this God-person relation have surfaced in the literature: (A) ‘God and Privacy’ by Falls-Corbitt and Michael McLain, and (B) ‘Privacy and Control’ by Scott Davison. I discuss key elements in both sides of this debate. Even though I will register my sympathy with both sides, I claim that both fail to grasp what I call the existential depth of the God-person relationship.

Personal Privacy in an Economic Age in Nigeria: A Need to Strike a Balance

The rise in the tide of information technology has led to an increase in the large amount of personal information that could be collected from an individual, thus breaching personal privacy. Protecting the individual personal information is paramount as it bothers on the dignity of the consumer and his right to solitude. While the Constitution of Nigeria provides for the protection of dignity of the Nigerian in section 37 and section 34 for protection of privacy sectoral laws are also in place to deal with the issue of personal privacy, the information collected from consumers most times have economic value in view of the growing economics in the country. These collected information are regarded as economically beneficial to the Data controller and even to the Data subjects themselves. Despite these economic advantage, there are still concerns on protecting personal information this has led to a policy debate, a need to strike the balance thus becoming necessary. This paper will attempt to analyze concerns and issues regarding personal privacy in view of growing economics and the policy issues surrounding it. This paper will make use of both primary and secondary sources of law.

A Comprehensive Study of Security of Internet-of-Things

Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT. Moreover, commercialization of IoT has led to public security concerns, including personal privacy issues, threat of cyber attacks, and organized crime. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this survey attempts to provide a comprehensive list of vulnerabilities and countermeasures against them on the edge-side layer of IoT, which consists of three levels: (i) edge nodes, (ii) communication, and (iii) edge computing. To achieve this goal, we first briefly describe three widely-known IoT reference models and define security in the context of IoT. Second, we discuss the possible applications of IoT and potential motivations of the attackers who target this new paradigm. Third, we discuss different attacks and threats. Fourth, we describe possible countermeasures against these attacks. Finally, we introduce two emerging security challenges not yet explained in detail in previous literature.

Hero on Twitter, Traitor on News

Is Edward Snowden a hero or traitor? In what ways do frames on social media and legacy news differ in covering the incident of Edward Snowden? Utilizing the approach of semantic network analysis, the study found social media users associated Snowden’s case with other whistle-blowers, bipartisan issues, and personal privacy issues, while professional journalists associated the Snowden incident with issues of national security and international relations. Frames on social media portray Edward Snowden as a hero while the frames on legacy news make him a traitor. The study further identified media frames on social media and legacy news differ in two ways: word selection and word salience. In addition, the study discussed the challenges and opportunities of framing analysis in the context of social media.

Against Autonomy: Justifying Coercive Paternalism

Back to table of contents Previous article Next article Book ReviewsFull AccessAgainst Autonomy: Justifying Coercive PaternalismJeffrey L. Geller, M.D., M.P.H., , and E. Fuller Torrey, M.D.Jeffrey L. GellerSearch for more papers by this author, M.D., M.P.H., Editor, and E. Fuller TorreySearch for more papers by this author, M.D.Published Online:1 Aug 2013https://doi.org/10.1176/appi.ps.640803AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail by ConlySarah; New York, Cambridge University Press, 2013, 206 pages, $85.50This book will be of great interest to mental health professionals who are involved with issues concerning involuntary hospitalization and medication. Those who favor such treatments will want their own copy; those opposed should borrow a copy only long enough to write a scathing denunciation. There are likely to be no middle-of-the-road readers of this book.Author Sarah Conly is a philosopher at Bowdoin College in Maine. She puts forward an argument “for paternalistic laws, and more specifically, paternalism of the sort that forces people to act, or refrain from acting, according to their best interests. I argue for the justifiability of coercive paternalism, for laws that force people to do what is good for them.” She specifically supports laws mandating seat belts for motorists and helmet use for motorcyclists, making cigarettes illegal, banning transfats from food, regulating portion size (such as was recently proposed in New York City for sodas), and other measures, which are sometimes characterized as “nanny state” attempts to improve personal health. To support her thesis, Conly reviews philosophical arguments favoring autonomy, such as John Stuart Mill’s On Liberty; the political dangers of giving government the authority to implement such laws; the issue of personal privacy; and evidence suggesting that people often act irrationally and contrary to their own best interests.What are the limits of Conly’s “coercive paternalism”? She says such measures must meet four criteria. First, the activity to be prevented must be “one that is opposed to our long-term ends,” such as health. Second, the preventive measure must be effective. Third, the benefits of coercion must outweigh the costs. And finally, “the measure in question must be the most efficient way to prevent the activity.” Thus Conly does not support the prohibition of alcohol because doing so was not effective and also because the costs outweigh the benefits.Although Conly says nothing about involuntary psychiatric hospitalization and medication, applying her criteria to these situations is useful. Individuals with serious psychiatric disorders who have anosognosia provide evidence of not thinking rationally, and being a danger to self or others is certainly not in their best interests. Coercive measures such as assisted outpatient treatment have proved effective. Whether the benefits of coercive paternalism are greater than the costs may be debated, but I personally believe they are, if coercive treatment is restricted to the small number of patients needing it and is subject to judicial overview. Finally, coercive treatment should be used only after other methods of persuasion have been tried and failed.In summary, for those interested in the issue of involuntary hospitalization and treatment, this is a good book to read on a leisurely afternoon when one can contemplate its philosophical and practical implications and ponder questions such as, “What should be the role of government?”Dr. Torrey is executive director at Stanley Medical Research Institute, and founder of the Treatment Advocacy Center, Chevy Chase, Maryland.The reviewer reports no competing interests. FiguresReferencesCited byDetailsCited byNone Volume 64Issue 8 August 2013Pages e02-e02 Metrics The reviewer reports no competing interests.PDF download History Published online 1 August 2013 Published in print 1 August 2013

Research of RFID System Security

Radio Frequency Identification (RFID) technology has been gradually into our daily production and life as a new non-contact automatic identification technology.However , the limited resources of low-cost RFID largely restrict the implementation of RFID security mechanisms,which leads to personal privacy issues ,and the issues greatly hinder the rapid adoption of the technology.Here, RFID communication model and security requirements are discussed,and then it analyzes the security solutions for low-cost RFID systems.

Restricted H.264/AVC video coding for privacy protected video scrambling

The issue of personal privacy has garnered significant attention with the extensive application of video surveillance systems. Privacy region scrambling is an effective method to protect privacy in video. To ensure that nonprivacy regions are not affected by scrambling, particular methods must be taken to prevent drift error in privacy protected video scrambling. However, existing methods have significantly reduced the coding efficiency. In this paper, we focus on improving coding efficiency while preventing drift error in privacy protected H.264/AVC video scrambling, which is the state-of-the-art coding standard. A restricted video coding scheme is proposed, which involves three parts of Mode Restricted Intra Prediction (MRIP), Search Window Restricted Motion Estimation (SWRME) and Boundary Strength Restricted Deblocking Filtering (BSRDF). Experimental results show that the proposed restricted video coding scheme prevents drift error with higher coding efficiency than others.

Emerging Neurotechnologies for Lie-Detection: Promises and Perils

Detection of deception and confirmation of truth telling with conventional polygraphy raised a host of technical and ethical issues. Recently, newer methods of recording electromagnetic signals from the brain show promise in permitting the detection of deception or truth telling. Some are even being promoted as more accurate than conventional polygraphy. While the new technologies raise issues of personal privacy, acceptable forensic application, and other social issues, the focus of this paper is the technical limitations of the developing technology. Those limitations include the measurement validity of the new technologies, which remains largely unknown. Another set of questions pertains to the psychological paradigms used to model or constrain the target behavior. Finally, there is little standardization in the field, and the vulnerability of the techniques to countermeasures is unknown. Premature application of these technologies outside of research settings should be resisted, and the social conversation about the appropriate parameters of its civil, forensic, and security use should begin.

Quantitative Analysis of Impacts of Moving toward a Vehicle Mileage-Based User Fee

This paper offers a broad examination of the revenue generation and social equity implications of a national mileage-based user fee that could be substituted for all or part of the current gas tax. Data from the 2001 National Household Travel Survey are combined with documented elasticity values that can be used to calculate changes in vehicle fleet composition and miles of travel by time of day in response to price signals. These data provide the basis for calculating the impacts of a mileage-based user fee system. It is found that modest mileage-based fees of just 0.5 cent per mile to 1.3 cents per mile can offer revenue streams that replace current gas tax revenue. In addition, the mileage-based user fee system appears to have minimal, if any, differential impacts across income classes and thus eliminates any potential equity concerns that may arise from the implementation of such a user fee system. Impediments to a mileage-based user fee system appear likely to be technological and personal privacy issues as opposed to transportation or social equity issues.

Emerging Neurotechnologies for Lie-Detection: Promises and Perils

Detection of deception and confirmation of truth telling with conventional polygraphy raised a host of technical and ethical issues. Recently, newer methods of recording electromagnetic signals from the brain show promise in permitting the detection of deception or truth telling. Some are even being promoted as more accurate than conventional polygraphy. While the new technologies raise issues of personal privacy, acceptable forensic application, and other social issues, the focus of this paper is the technical limitations of the developing technology. Those limitations include the measurement validity of the new technologies, which remains largely unknown. Another set of questions pertains to the psychological paradigms used to model or constrain the target behavior. Finally, there is little standardization in the field, and the vulnerability of the techniques to countermeasures is unknown. Premature application of these technologies outside of research settings should be resisted, and the social conversation about the appropriate parameters of its civil, forensic, and security use should begin.