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The article discusses the intersection of AI, art, and copyright, focusing on the legal and ethical implications of generative AI platforms. It highlights concerns about data scraping, copyright infringement, and the need for legislative responses. The text also mentions global initiatives, such as the G7 AI Principles, OECD AI Principles, and UNESCO's Recommendation on the Ethics of AI, to address the responsible use of AI technologies. The article concludes by emphasizing the importance of updating copyright laws to protect creators in the evolving landscape of AI-generated content. [Extracted from the article]

The article explores the pervasive nature of information in modern life, particularly how we are constantly surrounded by both large and small data. Topics discussed include the overwhelming volume of real-time data we receive through apps, notifications, and devices, the impact of this constant stream of information on our daily routines, and the way small, seemingly insignificant data shapes our perceptions and decisions.

Copyright of EURE is the property of Pontificia Universidad Catolica de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The emergence of data controllers as a novel term within data privacy laws, such as the General Data Protection Regulation (GDPR), has ushered in significant responsibilities. Stricter regulations prohibit the intentional sharing of personal records on the Internet. This research focuses on safeguarding data privacy, specifically in ubiquitous tabular formats across numerous websites. A novel approach employing a cell-key perturbation method is proposed, demonstrating efficacy in tabular formats. Addressing this challenge, we introduce the in-memory perturbation optimizer (IMPO) algorithm as a novel solution. The primary objective is to create and develop a platform that secures all personal data through a dispenser server, operating in near real-time. Also, it emphasizes the importance of balancing data utility with privacy protection to maintain the integrity and quality of the dataset. Experimental results reveal that the IMPO algorithm outperforms in terms of data accuracy. Additionally, the algorithm introduces an average time delay of 2 seconds, ensuring optimal time service for real-time datasets. [ABSTRACT FROM AUTHOR]

This article is an interview with Steven J. McDonald, the recently-retired General Counsel of Rhode Island School of Design (RISD), about the Family Educational Rights and Privacy Act (FERPA). McDonald discusses his career in higher education law, the importance of teaching FERPA to lawyers, and the differences between working in a public institution versus a private institution. He also emphasizes the need for registrars to build partnerships with general counsel and addresses the strengths and weaknesses of the current FERPA statute. McDonald believes that FERPA's technology-neutral approach is a strength, as it allows for the application of existing laws and policies to new technological developments. The text discusses various aspects of the Family Educational Rights and Privacy Act (FERPA) and its implications for higher education institutions. It highlights the need for clarity in defining who qualifies as a "student" under FERPA and the challenges of handling "inspect and review" requests that may be submitted for harassment purposes. The text also addresses the role of vendors and vendor contracts in relation to student data privacy and emphasizes the importance of working closely with legal counsel. It concludes by stating that FERPA is flexible and does not impose severe consequences for unintentional violations, encouraging readers not to fear it. [Extracted from the article]

Indoor thermal comfort is an important factor in ensuring travelers' well-being for hotels. This article explores its potential impact of thermal discomfort on hotel business travelers' wellness and work productivity. Employing a mixed-method approach, this study combines data from personal interviews and a follow-up scenario-based design survey to comprehensively examine the consequences of thermal discomfort. The timely understanding of how thermal discomfort may negatively influence business travelers' sense of wellness and productivity, and overall hotel satisfaction is crucial. This research offers valuable insights for hoteliers and policymakers in the pursuit of optimizing thermal environments within the hospitality and event sectors. [ABSTRACT FROM AUTHOR]

Copyright of Relações Internacionais is the property of Relacoes Internacionais and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Safeguarding personal digital data is crucial and requires appropriate training. However, privacy remains a novel topic, leaving teachers with limited guidance. This study investigates how elementary school students perceive personal data and assesses pre-service teachers’ accuracy in predicting children’s responses. Employing Nissenbaum’s contextual integrity framework, the outcomes of this research offer a nuanced perspective on privacy, considering different recipients and data types. The study surveyed 94 Grade 3 and Grade 5 students, asking them to indicate with which recipients (no one, parents, best friends, class, all other people) they would share specific information. In addition, 75 pre-service teachers were asked to indicate what they expected Grade 5 children would share with whom. The findings show: 1. what information Grade 3 and Grade 5 students consider to be most private, 2. which recipients they trust the most, 3. varying sharing practices between Grade 3 and 5, and 4. a tendency for pre-service teachers to underestimate children’s privacy sharing behavior. In the discussion section we propose five recommendations for enhancing digital privacy education. [ABSTRACT FROM AUTHOR]

Privacy concerns have led customer-centric companies to adopt fair information practices to protect their customers' private information. This situation forces organizations to understand how their consumers perceive their private data when they are disclosed or are already known by the company. This study aims to establish a classification of private data based on customer perceptions to allow companies to address rising privacy concerns. This study developed a two-dimensional map of the positioning of various pieces of information according to customer perceptions. A total of 157 observations were collected online, and multidimensional scaling was used as an analysis technique. The dimensions that explain the resulting classification or spatial map were the degree of exposure customers feel when their data are known and the likelihood that a specific piece of information can identify a particular person. [ABSTRACT FROM AUTHOR]

The General Data Protection Regulation has the main purpose of protecting personal data and ensuring the free flow of this data within the European Union. However, uniform application of the regulation across all Member States is essential to guarantee its effectiveness. In its 2020 report on the application of the regulation, the European Commission pointed out that there are significant differences between the procedures applied by national data protection authorities. These procedural differences constitute an obstacle to the harmonious and efficient functioning of the cooperation and dispute resolution mechanisms provided for in the regulation. These discrepancies can lead to legal uncertainties and uneven application of the regulation in different Member States, which undermines confidence in the data protection system at European level and can negatively affect both individuals and organizations operating across borders. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (English Edition) is the property of Journal of Donghua University Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the application of two regulatory frameworks of the European Union to Chinese investments in the energy sector: merger control focused on the protection of market competition and foreign investment control based on the protection of security and public order. First, it analyzes the application of the EU-wide merger control to Chinese mergers and acquisitions with a particular focus on the role of state-owned enterprises (SOEs) as primary actors in the implementation of China's industrial policies. Second, it discusses the evolution of the EU investment control framework aimed at addressing the security risks associated with foreign direct investment (FDI) in the European energy infrastructure and the acquisition of the associated critical technologies. [ABSTRACT FROM AUTHOR]

During the COVID-19 vaccination campaign, the federal government adopted a more centralized approach to the collection of public health data. Although the states previously had controlled the storage of vaccination information, the federal government's Operation Warp Speed plan required the reporting of recipients' personal information on the grounds that it was needed to monitor the safety of novel vaccines and ensure correct administration of their multi-dose regimens. Over the course of the pandemic response, this more centralized federal approach to data collection added a new dimension to pre-existing vaccination hesitancy. Requirements that recipients furnish individual information deterred vaccination among undocumented immigrants already fearful about the Trump Administration's data-driven immigration enforcement policies--even as undocumented essential workers faced enhanced risks of COVID-19 exposure. Disputes with some states over the federal government's proposed terms of governance for individual vaccination information compounded delays in the reporting of necessary public health information. Moreover, as the pandemic response evolved, the Biden Administration was obliged to counter apprehension among the broader public that federally-stored information might be used to enforce vaccination mandates or adoption of digital "vaccination passports." Notwithstanding calls for greater federal authority to directly gather data in future epidemics, I argue that the goal of achieving broad public vaccination uptake will be better served by preserving and improving a federalist approach that generally leaves the states to control the collection and storage of individually identifiable vaccination information. I contend that the lessons of COVID-19 suggest that more robust governance and technological controls for federal access to state public health data--coupled with improved transparency about the limits of federal data use--can both ameliorate public hesitancy and improve inter-governmental exchange. [ABSTRACT FROM AUTHOR]

Cloud computing offers an excellent platform for sharing information and services among departments, partners, customers, and other parties. To ensure the effectiveness of a cloud deployment, it is imperative that resources and data are available in a secure and efficient manner. The protection of personally identifiable information (PII) is a critical requirement for many organizations in order to prevent unauthorized disclosure of that information. In spite of the fact that private information may not be stored within an authorized organization, it can still be protected. It has been shown in recent literature that anonymity can be used to preserve PII privacy. The current methodology for anonymous access is based on certificates rather than subject identity or attribute-based encryption. Identity-based access control is inflexible and violates privacy laws. Access to a certificate can be linked using a certificate that poses a risk of re-identification. The types of objects that can be encrypted by attributes are limited. To preserve the privacy of attributes set of subject and object, we extend the attribute-based access control (ABAC) model by expressing privacy requirements with anonymous access. It inherits several characteristics from ABAC, including fine-grained authorizations, unlimited object types, policy flexibility, and object privacy. ABAC-PA2 uses homomorphic attribute-based signature (HABS), a secure anonymous access framework that enhances ABAC's identity-less and incorporates privacy-preserving principles by assigning privacy levels to resources. [ABSTRACT FROM AUTHOR]

Human activity recognition plays a vital role in various applications like healthcare, sports, and smart environments. ... With the increase in the use of smart wearables and sensors, the concern for privacy has increased drastically in recognizing the activities of individuals. The legacy techniques send the data from millions of users to a central server where the processing and model generation happens. This increases the load on the server. Federated Learning is a promising method to handle sensitive and personal data in an efficient manner as well as use distributed techniques to reduce challenges that are faced in legacy approaches. Edge devices like smartphones and embedded devices extract the raw data, preprocess it, and generate a personalized model. These models are then aggregated at a central server using federated learning methods and their aggregate model is sent back to clients. Long Short-Term Memory (LSTM) is used for model generation as it can effectively capture temporal dependencies in sequential data. In order to show a real-world scenario, an AWS EC2 instance is used as a server, and laptops and smartphones are used as clients. Our results demonstrate that such methods can be adopted and can be used on a much larger scale. The performance of the global and local models is comparable to those attained by conventional methods. The utilization of federated learning shows a significant step forward in the development of human activity recognition. This approach allows for collaborative learning across multiple devices while also ensuring the privacy of users is maintained. [ABSTRACT FROM AUTHOR]

With the exponential growth in connected smart devices that interchange sensitive, crucial, and personal data over the Internet of Things (IoT)-based cyberspace, IoT becomes undefended to cyber assaults. Intrusion Detection System (IDS) is a vital segment of security tool to detect cyber assaults. However, IDS produce high False Positive Rate (FPR) because IDS improves accuracy at the same time as it increases FPR. Therefore, a more effective cyber assaults detection system is needed. Based on the aforementioned issues, this paper proposes an approach to recognizing cyber assaults in the IoT environment. This approach first performs various data pre-processing steps. Next, important features are optimized using modified Binary Pigeon Inspired Optimizer. After that, optimized feature subset is fed separately to the monolithic classifiers such as k-Nearest Neighbours (kNN), Support Vector Machine (SVM), Decision Tree (DT) and bagging-based ensemble model such as Random Forest (RF) for detection of cyber assaults. In this study, UNSW-NB15 is used to test the success of the system, and related existing methods are compared with these results. Based on the results of the experiment, the proposed system with RF is the most effective in terms of accuracy (99.41%), F1-score (99.33%), detection rate (99.09%), and precision (99.92%) with a minimum FPR (0.03%) for a subset of optimized features (4 independent features out of 42). [ABSTRACT FROM AUTHOR]

Smartphones as a means of communication are not only used to send and receive messages and make phone calls. Today's smartphones are also intended to store personal data and sensitive information hence it is vulnerable to be illegally accessed by others. Fingerprint as a biometric lock screen for smartphones can be trusted to ensure the security and privacy of smartphone users. A fingerprint is closely related to a user's identity. When using fingerprints, users provide their personal biometric pattern, unknowingly giving their privacy and making them vulnerable to unauthorized use of their biometric pattern. This study is designed to reveal the factors that influence users when using fingerprints as a smartphone lock, using eight variables adapted from previous research. The data analysis used is Partial Least Squares (PLS) analysis. The results of this study suggest that trust, attitudes, and perceived risk are the factors that influence users when using their fingerprint as a smartphone lock. [ABSTRACT FROM AUTHOR]

This article presents an ethnographic account of the advocacy initiative, conducted by NGO PersonalData.IO and the company Hestia.ai, that seeks to empower gig workers by helping them regain access to their personal data through data access rights, using the European Union General Data Protection Regulation. It is based on a case study of Uber drivers in Geneva that has a worldwide relevance for the gig economy. Previously selfemployed, drivers are now classified as employees and their working time and earnings must be calculated according to local labour laws. We contribute to debates on algorithmic management in ride-hailing platforms by focusing on participatory methods of accountability through personal data, from an infrastructural perspective. First, we focus on the nexus between personal data protection and algorithmic management to understand the domination of ride-hailing platforms over the workers' means of production, i.e., their personal data. We provide empirical transparency on the data structures of Uber for the sake of algorithmic accountability. These structures are utilised for their surge pricing algorithms and ultimately govern the workforce. Second, within a collective process of governance, we built participatory tools and methods for empowering gig workers and data scientists. These are means for calculating earnings and working that made explicit a new social meaning of work, i.e., "lost time between rides". [ABSTRACT FROM AUTHOR]

The paper shows that at the present stage of digitalization development, the issue of personal data protection in the educational sphere by means of information technology tools is important. Based on the analysis of information interactions in the educational environment, the author identifies the categories of parties (subjects) which may be affected by the loss of personal data in such interactions in the context of digitalization. The most vulnerable category to the loss of personal data is identified – they are so called- eduction searchers. The main objects involved in the exchange of information in the educational sphere are presented. The application of the expert method to determine the priority of the object of information interactions is proposed. It is established that this is the "Data Broker". Based on the information obtained, a model of information interaction in the educational environment in the context of digitalization and a model for determining the loss of personal data during digitalized information integration in the field of education have been developed. Prioritised subjects in such integrations are identified. A model for protecting personal data of educational stakeholders in the context of digitalization with the help of information technology tools has been developed. The basic requirements for an information tool are highlighted. The developed model will make it possible to develop the issues of information protection, confidentiality, integrity and availability of data in the context of digitalization of the educational sphere. [ABSTRACT FROM AUTHOR]

Augmented and virtual reality technologies are actively making their way into the field of healthcare, revolutionizing traditional approaches to diagnosis, prevention, and treatment. This article discusses how these concepts are combined and their profound impact on the emergence of the Metahospital. The authors provide an overview of this concept, highlighting its dual role in aiding healthcare professionals and empowering patients. Central to this paradigm shift is technology's potential to enhance specialized training and elevate the precision and efficiency of diagnostics, all while prioritizing patient safety. This model's core features are expounded upon, with a keen emphasis on ethical considerations and safeguarding personal data, a critical aspect explored within the paper. Furthermore, the article elaborates on the creation of a surgical simulator tailored for procedures like angioplasty and coronary artery stenting, showcasing the tangible advancements within the realm of medical technology. [ABSTRACT FROM AUTHOR]

The emergence of blockchain has ushered in a significant transformation in information systems research. Blockchain’s key pillars such as decentralization, immutability, and transparency have paved the path for extensive exploration in various research domains. This particular study is focused on electronic voting, aiming to improve voting procedures by making better use of the benefits offered by blockchain technology. Through a comprehensive review of existing literature, we highlight the potential benefits of blockchain-based electronic voting systems such as transparency, security, and efficiency. However, several challenges, such as scalability, personal data confidentiality, and ensuring robust identity verification, persist. Addressing these issues is necessary to unlock the full potential of blockchain-based electronic voting systems, thereby fostering the development of trustworthy election systems in the future. [ABSTRACT FROM AUTHOR]

Copyright of EurAmerica is the property of EurAmerica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Covid-19 coronavirus pandemic has affected higher education institutions all over the world, causing face-to-face education to cease. Schools have tried to carry out educational activities through online teaching either by using on premise infrastructure or by leasing cloud based online platforms. Although these platforms are convenient, most of them do not meet all the requirements for higher education institutions. Not knowing where the personal data is stored in a public cloud creates another problem for some countries according to laws. In this study, a new online learning platform has been developed for higher education institutions to solve these problems using state-of-the-art cloud technologies. The new system enables implementation of individual curricula of many higher education institutions in one software system, and it can be taken into service quickly in emergencies. It expands dynamically by activating a large number of streaming servers to meet the demand. The new system provides easy to use-learn interfaces, offers an economical solution for e-learning by sharing the resources, and compliant with the law on protection of personal data. The new platform was in service at 12 universities in Turkiye during the fall 2020, and its performance was measured with surveys at various levels. [ABSTRACT FROM AUTHOR]

To provide comprehensive protection of personal data and data subjects, the General Data Protection Regulation (GDPR) has addressed various aspects. One of these protections is to pay attention to personal data that due to their sensitive nature, need more protection (specific personal data). This regulation sets out various requirements for the processing of specific personal data. These requirements include the principle of prohibition of sensitive personal data processing unless there is a legal exception. Another requirement is the need for official authority oversight and legal authorization to process personal data relating to criminal convictions and offences. Determining a certain age for consent to processing is also a requirement for protecting children’s personal data. In this study, by explaining in detail these requirements and clarifying how specific personal data is processed in EU law, the protection of specific personal data in Iranian law has also been examined. The conclusion is that although Iranian law on personal data does not have an independent law to rely on to protect specific personal data, however, according to the laws and regulations, legal doctrine, principles of Iranian law, there are many requirements for the processing of specific personal data set out in the GDPR, also in Iranian law. [ABSTRACT FROM AUTHOR]

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution's work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel's location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the process of digital transformation of public administration in Romania and the European Union, in the current legislative and institutional context. The reform of public administration, in the context of the digitization of the public sector, is focused on simplifying procedures and bureaucratic burdens for individuals and legal entities, increasing the efficiency and effectiveness of the administrative act, and ensuring accessibility and transparency of data use by public authorities and institutions. At the same time, this study also deals with issues related to eGovernment, cybersecurity, and the security of personal data of users of platforms and information systems. [ABSTRACT FROM AUTHOR]

Copyright of Ankara Barosu Dergileri is the property of Ankara Bar Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Personal data play a fundamental role in contemporary socioeconomic dynamics, with one of its primary aspects being the potential to facilitate discriminatory situations. This situation impacts the knowledge organization field especially because it considers personal data as elements (facets) to categorize persons under an economic and sometimes discriminatory perspective. The research corpus was collected at Scopus and Web of Science until the end of 2021, under the terms "data discrimination", "algorithmic bias", "algorithmic discrimination" and "fair algorithms". The obtained results allowed to infer that the analyzed knowledge domain predominantly incorporates personal data, whether in its behavioral dimension or in the scope of the so-called sensitive data. These data are susceptible to the action of algorithms of different orders, such as relevance, filtering, predictive, social ranking, content recommendation and random classification. Such algorithms can have discriminatory biases in their programming related to gender, sexual orientation, race, nationality, religion, age, social class, socioeconomic profile, physical appearance, and political positioning. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos de Música, Artes Visuales y Artes Escénicas is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It would not be an overstatement to say that it can be arduous to keep up with the pace of innovation in the Decentralized Finance ("DeFi") domain. One such DeFi instrument gaining traction is the self-custodial digital wallet. Through the course of this paper, we endeavor to analyze the respective proposals set forth by the European Parliament's Committee on Economic and Monetary Affairs ("ECON") and the U.S. Treasury's Financial Crimes Enforcement Network ("FinCEN")--both of which mandate the collection and verification of personal data concerning the owners of self-custodial wallets by centralized cryptocurrency exchanges through verified providers. Furthermore, we shall assess these proposals' congruency with their respective jurisdiction's data-privacy legislation to ascertain how the user-privacy agreement is being infringed upon. Finally, we will demonstrate the need for a novel governance model via Blockchain-based Decentralized Autonomous Organizations ("DAO") and how decentralized autonomous governance is the future for law-making, facilitating unprecedented decentralized regulatory solutions. To conclude, we propose the development of an adroit regulatory system--a framework free from information oligarchies and management manipulations. [ABSTRACT FROM AUTHOR]

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities. [ABSTRACT FROM AUTHOR]

In this paper, we mean by digital health passport a digital passport or immunity passport that stores information about an individual's personal data, health status, travel history, health conditions, and medical and drug records. The proposed innovative scheme can ease travel, ensure free movement, and minimize economic damages. However, protecting personal and digital health passport information during a crisis is challenging. Therefore, this paper aims to propose an agent-based secure decentralized innovative digital health passport protocol that is interoperable, fault-tolerant, and compatible with privacy standards. The proposed solution is known as Secure Mobile Digital Passport Agent (SMDPA), uses: (1) Private Set Intersection (PSI) to (a) protect Digital Health Passport Information (DHPA) against surveillance, especially during the crisis, (b) generate Bit Passing Coin (BPC); (2) blockchain to store serialized SMDPAs copies (3) mobile agent to disseminate DHPA autonomously; (4) Location and time attributes for data minimization. We prototype the proposed solution using the JADE agent framework. The performance measurements show that the result is acceptable. [ABSTRACT FROM AUTHOR]

Data sharing poses a number of privacy risks, including reconstruction attacks, model inversion attacks, and membership inference attacks. Federated learning is a distributed paradigm that aims to improve privacy by sharing parameter gradients rather than the original data. The purpose of this study was to use federated learning and differential privacy techniques to train a model on sensitive personal opinion data while protecting the privacy of individual participants. The experiments carried out evaluate the effect of various noise levels on the model's performance. As the number of training rounds increases, the model's performance improves continuously, with noise multipliers and clients per round adjusted to achieve a balance between privacy and accuracy. This study adds to the literature on data analysis in scenarios with limited data sharing protocols and illustrates the ability to solve data privacy difficulties and restrictions efficiently using federated learning and differential privacy techniques. [ABSTRACT FROM AUTHOR]

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This paper concerns the future - a scenario that is not so distant in time, as the future starts today. With the increasing datafication of our society and the approval of the European Union Data Governance Act that establishes conditions and safeguards to encourage the free flow and reuse of data for scientific, economic and societal progress, we are left wondering whether the existing challenges surrounding personal data management will be solved by then - or whether they will only be exacerbated. This sci-fi short story depicts a future where individuals, since a young age, question whether they still retain agency over their lives and their destinies, given that data-hungry personalized services surveil them extensively and steer their personal development. Returning to science fiction narratives to explore a societal issue at the edge of technology and law has a threefold purpose, in: (1) developing critical skills and exploring future consequences safely through the imagination; (2) fostering foresight and proactivity in policy-making; and (3) democratizing the debate about technological developments that concern us all. If this story depicts a desirable or undesirable future is left to the readers to assess. [ABSTRACT FROM AUTHOR]

Copyright of Salud Uninorte is the property of Fundacion Universidad del Norte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the information age, the analysis of human behavioral patterns is of significant interest, and extensive data collection is crucial in the context of privacy protection. From 2011 to 2023, the authors collaborated with more than 20 volunteers to construct and collect 30,791 lifelog using mobile devices; these lifelog included a sizable amount of multi-type data containing behavioral activity information, temporal location information, textual description information, and media images. In this paper, we propose a DCP-BiLSTM (Description + Cluster + Position) And Bidirectional Long-Short Term Memory (BiLSTM) personal big data behavior classification model for the lifelog dataset, which uses text description information + GPS information classified by DB-SCAN + location information, it is validated on Liulifelog to better address the behavior prediction problem of lifelog data. Unlike the traditional text classification approach, which is based on a unique lifelog dataset, BiLSTM can combine bidirectional semantic variables comprising description information and location information into the DCP-BiLSTM model. Evaluation of the DCP-BiLSTM demonstrates that can increase the performance and accuracy of behavior prediction, and the BiLSTM that incorporates description and location information to build, not only identifies daily user activity but also corrects the bias caused by manual analysis's one-sidedness and has greater prediction performance. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Review of Private Law / Revista Română de Drept Privat is the property of Universul Juridic Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Covid-19 and the public health policies emerging in response have laid bare a multiplicity of issues related to educational access and knowledge equity on a global scale. Among these, the quick shift to online and hybrid education models led teachers to adapt a plethora of digital platforms to deliver content and sponsor interactions). Such platforms range from institutionally sanctioned (and subscribed) Learning Management Systems (LMSs) to software provided by organizations beyond the institution and can pose a threat to student data and privacy. Data surveillance in educational contexts is not a new issue, nor is it only a strictly digital problem. However, the current milieu of constant and continuing public health crises has led to more frequent, uncritical, and hurried adoption of learning technologies. This article challenges professionals in higher education specifically to take a more critical look at the various EdTech platforms they are, have, and will adopt in the post-COVID-19 era, and the spectrum of surveillance such platforms enact. Through a review of common entities such as LMSs, Google Workspace for Education, and Zoom video conferencing software, this article demonstrates how these technologies place both teachers and students in a relationship to data and learning characterised by “epistemic inequality” or “unequal access to learning imposed by private commercial mechanisms'. By taking a closer look at the problematic surveillance functioning across EdTech, this article makes a case for Commons-based Peer Production communities as equitable, open educational alternatives that have resisted market-based neoliberalism and surveillance capitalism. Practitioner Notes 1. The Covid-19 pandemic led to dramatic increases in Edtech adoption at all levels of education. Such adoption was often hurried and uncritical, furthermore. 2. Both traditional Edtech such as Learning Management Systems (LMSs) and more recent educational and communication platforms introduced by “big tech” actors such as Zoom and Google sponsor unethical surveillance and datafication of students, leading to dehumanizing educational models. However, such surveillance exists on a spectrum and LMSs are much less exploitative than “big tech” projects. 3. Educators at all levels, but especially postsecondary, should practice criticality when adopting and using Edtech, and offer students opportunities to better understand issues related to surveillance, privacy, and data extraction. 4. Educators should consider engaging students in more ethical models of digital and non-digital education platforms and projects, such as those offered by Commons-based Peer Production (CBPP) projects like Wikipedia. [ABSTRACT FROM AUTHOR]

THE LATEST TRENDS IN USE OF PERSONAL DATA BY THE ENTITIES OF FINANCIAL SECTOR MAY BRING ADVANTAGES FOR THE END-USER AS A TAILOR-MADE OFFER OF FINANCIAL SERVICES MAY BE MORE APPROPRIATE AND CHEAPER. BUT THIS OPPORTUNITY INCLUDES ALSO SOME RISKS AS THE AMOUNT OF DATA CENTRALIZED BY THE FINANCIAL INSTITUTIONS OR THE EXTEND TO WHICH THOSE DATA ARE USED MAY BE RESPONSIBLE FOR NEGATIVE RESULTS (CYBER-CRIME OR REJECTION OF CUSTOMERS). THE ARTICLE IS INTENDED TO EMPIRICALLY EVALUATE WHETHER THERE IS A LINK BETWEEN THE IMPROVEMENT IN THE GDPR USE AND THE FINANCIAL LITERACY OF THE FINAL CONSUMER OF FINANCIAL SERVICES. BASED ON A QUALITATIVE ANALYSIS OF THE REGULATION IMPLEMENTED AT INTERNATIONAL LEVEL, THE PAPER SUMMARIZES THE MAIN CONCERNS OF THE NATIONAL AUTHORITIES IN ORDER TO IMPROVE THE PROTECTION OF THE CUSTOMER BY RAISING AWARENESS OF THE USE OF HIS/HER PERSONAL DATA BY THE FINANCIAL SERVICE PROVIDERS. THE ANALISYS MAY BE USEFUL FOR THE PERSONAL DATA OPERATORS AS THEY MAY ADAPT THEIR INTERNAL POLICY TO BE COMPLIANT WITH THE LATEST PROVISIONS. [ABSTRACT FROM AUTHOR]

The European General Data Protection Regulation 2016/679 (GDPR) aims to ensure the security and privacy of individuals in the European Union (EU). Companies located within and outside of the EU must comply with GDPR when processing personal data of EU citizens. Medical writing includes the development of documents related to clinical research. To develop those documents, medical writers have access to personal data, including health information considered as sensitive data. Therefore, medical writing falls within the purview of GDPR and must comply with its requirements. This article is an overview of the impact of GDPR on medical writing including security measures such as anonymisation, pseudonymisation, and data minimisation techniques. It also provides an overview of the technical and organisational actions in the framework of medical writing to guarantee respect of data subjects' rights and freedoms. [ABSTRACT FROM AUTHOR]

The paper presents a study on how Czech news consumers adapted to the personalization of news. Our study is the first one that tries to deepen the existing knowledge about Czech internet users’ reflections of the algorithmic news contents’ personalization. The goal of the research was to gain insight into consumers' perceptions of news personalization and to identify the needs and competencies they require to protect their personal data. The research is based on three focus groups consisting of 27 participants, two of which were ordinary users from the Czech Republic, and one consisting of experts from the same country. The qualitative analysis reveals that respondents see news personalization as a loss of control over search mechanisms and information delivery. They express doubts about the credibility of personalization and describe the respective algorithms as a "black box." The study outlines defensive tactics used by users to resist personalization algorithms, and the data indicates that readers view the process of algorithmic news personalization as a threat to the management of their personal data. [ABSTRACT FROM AUTHOR]

The purpose of this study is to answer the question of what options companies have when competing for digital talent. SMEs in particular, with their limited resources and significant backlog in terms of the digitalisation of corporate processes, find themselves in a digital talent trap which threatens to grow ever larger as digitalisation progresses. In other words, the digital talent gap could become a digital talent trap for SMEs in terms of digitalisation. The aim of this work is to derive from this insight a pragmatic and costeffective solution which, in addition to the highly competitive “buy” option – i.e., classic recruitment via the labour market – offers a “make” option for SMEs that focuses more on their own employee resources. The research approach was based on two steps. In the first step, various literature approaches for recording and measuring digital skills were analysed in order to summarise them and derive potential digital talent profiles. The second step was to search for suitable software packages that are able to detect these profiles or skills in company documents. With these data sets, an analysis was carried out in the third step using AI algorithms – created in Python – in order to identify potential digital talents. An anonymised personal data set was used to test the above decision-making process. The findings show that SMEs could already access powerful, user-friendly and low-cost digital AI tools when searching for digital talent – especially in the search for digital talents within the company. The assumption that only larger companies with the corresponding financial resources can afford this option cannot be confirmed. The originality of the work lies in the finding that suitable AI tools exist for SMEs to search for digital talent, but that these are not currently being used extensively. Barriers such as prohibitively high costs or the low user-friendliness of AI tools could not be confirmed within the scope of this study. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is designed to evaluate the privacy risks of personal data practices. • Privacy indicators are defined in terms of personal data based on standards. • A privacy policy analysis model is designed to identify data practices for evaluation. • The privacy indicators of mobile applications are evaluated with real-world datasets. Personal data have become the key to data-driven services and applications whereas privacy requirements are now strongly imposed by regulations. Meanwhile, people find it difficult to understand whether the services and applications handle personal data to comply with their agreements and regulations. Therefore, the need for privacy indicators, which summarize privacy contents as forms of privacy scoring, labels, etc., has increased to empower the users' rights by providing understandable information about privacy. For firm privacy indicators, proper criteria and methods for evaluating the level of privacy risks and compliance are required. Accordingly, this paper proposes a privacy scoring framework for services in the context of handling personal data, inspired by six standardized indicators. This paper introduces detailed information on standardized indicators and proposes privacy indicators to quantify privacy scores. Also, this paper proposes methods for evaluating privacy policy based on a set of machine learning-based hierarchical binary classifiers and processes for quantifying the level of privacy risks and compliance from privacy-related information. Through analyzing privacy policies and data access lists of more than 10,000 mobile applications on Google Play Store and investigating case studies on privacy scoring of some mobile applications, this paper shows the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]