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Cookies are an essential part of today's internet. In the EU, the ePrivacy Directive (read with the EU GDPR) requires prior informed consent for using cookies. Usually, websites use cookie banners to meet the informed consent requirement. However, due to a lack of harmonisation of cookie law across the EU, varying cookie guidelines by data protection authorities of different member states, and varying implementation of cookie consent requirements by websites, users face differing cookie banners on different websites. Having to face differing cookie banners requires additional cognitive effort and time to read, understand, evaluate, and select options on each banner. Moreover, the way consent is sought remains deceptive. Websites use dark patterns, vague language, positive framing, and nudging to elicit user consent. This paper proposes standardised cookie banners to solve the various problems with cookie consent banners. The paper suggests that the standardised banner must have standardised timing of display, position on the website, text and language, presentation, consent options, and way of consenting. [ABSTRACT FROM AUTHOR]

• Evaluated pedestrians' and bicyclists' perceptions on six AV regulations. • Examined pedestrians' and bicyclists' perceptions of testing AVs in public streets. • Identified strong support for AV-related data sharing regulations. • Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. • AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. Introduction: Development and implementation of autonomous vehicle (AV) related regulations are necessary to ensure safe AV deployment and wide acceptance among all roadway users. Assessment of vulnerable roadway users' perceptions on AV regulations could inform policymakers the development of appropriate AV regulations that facilitate the safety of diverse users in a multimodal transportation system. Method: This research evaluated pedestrians' and bicyclists' perceptions on six AV regulations (i.e., capping AV speed limit, operating AV in manual mode in the sensitive areas, having both pilot and co-pilot while operating AVs, and three data-sharing regulations). In addition, pedestrians' and bicyclists' perceptions of testing AVs in public streets were evaluated. Statistical testing and modeling techniques were applied to accomplish the research objectives. Results: Compared to the other AV regulations assessed in this research, strong support for AV-related data sharing regulations was identified. Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. Conclusions: Policymakers and AV technology developers could develop effective educational tools/resources to inform pedestrians and bicyclists about AV technology reliability and soften their stance, especially on AV regulations, which could delay technology development. Practical Applications: The findings of this research could be used to develop informed AV regulations and develop policies that could improve pedestrians' and bicyclists' attitudes/perceptions on regulating AVs and promoting AV technology deployments. [ABSTRACT FROM AUTHOR]

On 25 October 2021, Nigeria became the second country in the world, and the first in Africa, to launch a central bank digital currency. Launched with the tag line "Same Naira. More possibilities", the Central Bank of Nigeria publicized the eNaira as having the capability to deepen financial inclusion, reduce the cost of financial transactions and support a more efficient payment system. However, more than one year after its launch, its usage is yet to gain a critical mass. This article identifies the significant challenges that make the eNaira unacceptable and potentially ineffective. First, its status as legal tender is questionable; secondly, it undermines privacy, a critical component of physical cash. Thirdly, it is incapable of wide acceptance by individuals and entities across Nigeria. The article explains each of these challenges and proposes a roadmap to the eNaira's acceptance and effectiveness. [ABSTRACT FROM AUTHOR]

As the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future. [ABSTRACT FROM AUTHOR]

The rapidly evolving field of biometric recognition and verification technologies has seen substantial progress recently. However, the storage of biometric data in cloud environments presents significant security challenges, including data theft and unauthorized modifications. Cancelable biometrics emerge as a strategic solution to these issues. This study examines the efficacy of diverse cancelable biometric methods, classified into transformation, encryption, and hybrid approaches. We introduce a novel cancelable biometric system that combines a hybrid optical asymmetric phase truncated Fourier transform (PTFT) with a symmetric cipher scheme, enhanced by a two-level block-based shuffling technique. This innovation improves security through a streamlined shuffling process that divides the PTFT output into four sections for shuffling and an exclusive OR operations with a unique key, enhancing security and computational efficiency. Our empirical evaluation on three distinct datasets (Faces, Iris, and Fingerprints) from FERET, UPOL and CASIA, and FVC2002 DB1 and DB2, respectively, demonstrates superior performance of our system compared to traditional PTFT-based systems. Notable performance metrics include an Area under the receiver operating characteristic curve of 0.9998. Significant improvements were also observed in false acceptance and false rejection ratios, along with more uniform histogram distributions, thereby enhancing randomness in the resultant deformed patterns. From a security standpoint, our system exhibits a high entropy of 7.95 and achieves near-optimal results in the number of pixels change rate and unified average changing intensity, with values of 98.9115 and 23.9755%, indicating robust resilience against potential differential attacks. The research findings, supported by detailed tables and graphical illustrations, underscore the additional security benefits offered by the two-level encryption mechanism. [ABSTRACT FROM AUTHOR]

Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user's identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder. [ABSTRACT FROM AUTHOR]

With more personal devices being connected to the internet, individuals are becoming increasingly concerned about privacy. Therefore, it is important to develop machine learning algorithms that can use customer data to create personalized models while still adhering to strict privacy laws. In this paper, we propose a robust solution to this problem in a distributed, asynchronous environment with a verifiable convergence rate. Our proposed framework trains a Convolutional Neural Network on each client and sends the feature embeddings to other clients for data aggregation. This allows each client to train a deep-learning model on feature embeddings gathered from various clients in a single communication cycle. We provide a detailed description of the architecture and execution of our suggested approach. Our technique's effectiveness is evaluated by comparing it to the top central training and federated learning (FL) algorithms, and our tests on diverse datasets demonstrate that our method outperforms FL in terms of accuracy and is comparable to central training algorithms. Our findings also show that our proposed method reduces data transfer by over 75% compared to FL, resulting in significant bandwidth savings. As a result, model training can assist companies with high security and data protection concerns in setting up reliable collaboration platforms without requiring a central service provider. [ABSTRACT FROM AUTHOR]

A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a research article titled "Research using population-based administration data integrated with longitudinal data in child protection settings: A systematic review." The correction addresses missing ORCID iDs for three authors and errors in the headings for Table 2 in the PDF version. The table provides information on various studies related to child protection, including the authors, research area, data sources, and linkage quality. The studies cover topics such as mental health, education, drugs and alcohol, domestic violence, and health insurance. The correction notice provides the correct information for the authors' ORCID iDs and directs readers to the complete and accurate Table 2. [Extracted from the article]

In this paper we propose the idea that compliance of a producer of official statistics to the basic principles of data protection in the preparation of a survey, while imposing additional burden, complexity or even restrictions to the process, can actually prove to be an effective tool to ensure and demonstrate compliance with the Fundamental Principles of Official Statistics and the European Statistics Code of Practice. To show the validity of this idea we utilize the Greek Population and Housing Census of 2021, conducted in 2021 by the Hellenic Statistical Authority (ELSTAT), as a representative example of statistical survey, which was built from the beginning with a primary look to the basic data protection principles of the General Data Protection Regulation of the European Union (GDPR). While preparing the Census we came across the intrinsic interplay that exists between principles of the GDPR and the Fundamental Principles and the Code. In the paper we analyze this interplay as we came across it in the Census and show that it can be used as a tool to strengthen the implementation of basic principles of official statistics in all statistical production processes. [ABSTRACT FROM AUTHOR]

Background: Use of electronic methods to support informed consent ('eConsent') is increasingly popular in clinical research. This commentary reports the approach taken to implement electronic consent methods and subsequent experiences from a range of studies at the Leeds Clinical Trials Research Unit (CTRU), a large clinical trials unit in the UK. Main text: We implemented a remote eConsent process using the REDCap platform. The process can be used in trials of investigational medicinal products and other intervention types or research designs. Our standard eConsent system focuses on documenting informed consent, with other aspects of consent (e.g. providing information to potential participants and a recruiter discussing the study with each potential participant) occurring outside the system, though trial teams can use electronic methods for these activities where they have ethical approval. Our overall process includes a verbal consent step prior to confidential information being entered onto REDCap and an identity verification step in line with regulator guidance. We considered the regulatory requirements around the system's generation of source documents, how to ensure data protection standards were upheld and how to monitor informed consent within the system. We present four eConsent case studies from the CTRU: two randomised clinical trials and two other health research studies. These illustrate the ways eConsent can be implemented, and lessons learned, including about differences in uptake. Conclusions: We successfully implemented a remote eConsent process at the CTRU across multiple studies. Our case studies highlight benefits of study participants being able to give consent without having to be present at the study site. This may better align with patient preferences and trial site needs and therefore improve recruitment and resilience against external shocks (such as pandemics). Variation in uptake of eConsent may be influenced more by site-level factors than patient preferences, which may not align well with the aspiration towards patient-centred research. Our current process has some limitations, including the provision of all consent-related text in more than one language, and scalability of implementing more than one consent form version at a time. We consider how enhancements in CTRU processes, or external developments, might affect our approach. [ABSTRACT FROM AUTHOR]

What does it mean to go beyond pseudonyms? Building on fieldwork with privacy and data protection advocates in Germany, this article incorporates ethical imperatives coming from within and beyond the field, to develop a different mode of writing it calls spirit portraiture. This entails characterizing natural persons, without including any personal data through which they could be re‐identified, with or without the use of computing. Drawing on three ethnographies that already go beyond pseudonyms, it repositions the axis of representation away from revelation and concealment—that pivots around a monist notion of empirical truth—towards opacity and legibility—that pivots instead around its own point of intervention. [ABSTRACT FROM AUTHOR]

This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as 'prediction power'. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of 'predictive privacy' as a protected good based on collective interests. [ABSTRACT FROM AUTHOR]

A master copy is an artifact whose quality or value carries over to the copies it engenders. In the United Arab Emirates, home to a marketplace trade in Pashto‐language film and music, master copy also refers to the context of the artifact's accessibility; it describes a print made from a medium—such as a celluloid film, an audiocassette, or a vinyl record—considered to hold the earliest or highest‐quality recording. Situated at a narrowing analog‐to‐digital bottleneck, a master copy is not an original but an interface between the social circumstances afforded by storage and communication media. Thus, as distinct from the formats that manifest content or preserve data, bottlenecks and interfaces ought to be studied on their own terms, as is shown in ethnographic research on master copies that travel between Pakistan and Afghanistan, and beyond, throughout the Pashtun diaspora. [ABSTRACT FROM AUTHOR]

Access control systems have become integral to every organization's defensive arsenal to protect sensitive information and ensure that businesses comply with data privacy regulations. The goal is to block out anyone who shouldn't be able to get their hands on sensitive information. Authorization solutions like access control help keep sensitive information safe by enforcing strict rules against unauthorized users. This study investigates the difficulties of pushback request response management in P2P electronic health records (EHRs) and suggests a blockchain-enabled EHR for a decentralized P2P healthcare facility. To prevent privacy breaches in the blockchain network and to make information more easily accessible, a new method of authentic access control has been created and implemented. The PRR is an innovative method proposed where security-based mathematical representation and modelling on EHR datasets create a proven security aspect of data privacy. We tested on-chain and off-chain computing modalities by implementing them. Simulations with our implementation show that our suggested pushback request response to medical data significantly lowers the overall peak request. The off-chain computing method offers lower delay time and overhead than the on-chain mode while maintaining the same level of system integrity. In addition, we evaluated the suggested method in terms of its performance and security, and we compared it to an existing hybrid method while taking into account data from the New York State Department of Health. [ABSTRACT FROM AUTHOR]

According to Thelwall's ([2011]. "Privacy and Gender in the Social Web." In Privacy Online. Perspectives on Privacy and Self-Disclosure in the Social Web, edited by S. Trepte and L. Reinecke, 251–266. Springer) social web gendered privacy model, gender differences in offline privacy risks (i.e. experiences of privacy threat, such as aggressive behaviour or betrayal of a secret) and communication qualities transfer to online contexts, and shape gender differences in online privacy perceptions and behaviours. Using representative data (n = 1,043) from four times of measurement over the course of three years, a structural equation model was set up. I found that people with negative offline privacy experiences at T1 express higher online privacy concerns a year later (T2), and take more actions to protect their online privacy at T3. When adequate privacy protection is established, people disclose more personal information privately (e.g. messenger), but not in public (e.g. status updates) at T4. Females reported more negative offline privacy experiences, offline social support, and offline information disclosure. In contrast to the model's claims, in an online context, men disclose more personal information both privately and publicly. The results provide evidence for the proposed relations of Thelwall's (2011) model: Offline conditions transfer to online contexts and shape social media users' privacy perception. However, the findings do not support the idea that women are an especially vulnerable group in online settings. [ABSTRACT FROM AUTHOR]

The article "Health Data on the Internet" discusses the increasing interconnection of health systems via the internet and the associated privacy risks. It emphasizes that the protection of sensitive health data must be ensured in order to prevent personal harm and criminal activities. The article examines various applications in the healthcare sector that utilize the internet and sheds light on the legal framework for the processing of health data according to the General Data Protection Regulation (GDPR). Various aspects of data protection in healthcare are addressed, such as the use of search engines, doctor rating platforms, fitness and health apps, online support groups, appointment management in healthcare, external service providers, video consultations, digital health applications (DiGAs), and the electronic patient record (ePA). It is pointed out that many data protection questions are still unresolved and that additional security measures are necessary to ensure the protection of health data. [Extracted from the article]

High leakage power consumption has become one of the main concerns of data security protection with CMOS device scaling. Spintronic technology is one of the efficient solutions to control circuit leakage power consumption by benefiting from its non-volatility property. Spintronic devices such as magnetic tunnel junctions (MTJs) are also compatible with CMOS transistors and suitable for designing hybrid MTJ/CMOS circuits. Hybrid MTJ/CMOS circuits can be used in many applications, such as logic-in-memory (LiM) and high-security structures. These circuits demonstrate enhanced data security compared to CMOS-based circuits by leveraging their non-volatile feature, making information leakage and data access more challenging. However, these circuits are not completely immune to attacks, and information can be revealed by potent power analysis attacks such as differential power analysis (DPA) and correlation power analysis (CPA). This paper investigates the resistance to side-channel attacks, especially DPA and CPA, for the magnetoresistive RAM (MRAM) and hybrid MTJ/CMOS AND, OR, XOR gates, hybrid MTJ/CMOS full-adder, and multiplexer circuits. Comprehensive post-layout simulation results using 40 nm TSMC CMOS technology, Monte-Carlo results, and values of normalized energy deviation (NED), normalized standard deviation (NSD)), and normalized power deviation (NPD) of PCSA-based MRAM indicate that the power consumption pattern remains constant in circuits with symmetrical structures in the reading phase even in the presence of inevitable process variation, so these circuits are resilient to power attacks and are competent for use in high-security applications such as hardware implementation of encryption algorithms. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

Phosphorus is indispensable for plant growth and development, with its status crucial for determining crop productivity. Plants have evolved various biochemical, morphological, and developmental responses to thrive under conditions of low P availability, as inorganic phosphate (Pi), the primary form of P uptake, is often insoluble in soils. Over the past 25 years, extensive research has focused on understanding these responses, collectively forming the Pi starvation response system. This effort has not only expanded our knowledge of strategies to cope with Pi starvation (PS) but also confirmed their adaptive significance. Moreover, it has identified and characterized numerous components of the intricate regulatory network governing P homeostasis. This review emphasizes recent advances in PS signaling, particularly highlighting the physiological importance of local PS signaling in inhibiting primary root growth and uncovering the role of TORC1 signaling in this process. Additionally, advancements in understanding shoot-root Pi allocation and a novel technique for studying Pi distribution in plants are discussed. Furthermore, emerging data on the regulation of plant-microorganism interactions by the PS regulatory system, crosstalk between the signaling pathways of phosphate starvation, phytohormones and immunity, and recent studies on natural variation in Pi homeostasis are addressed. [ABSTRACT FROM AUTHOR]

In the realm of federated learning (FL), the exchange of model data may inadvertently expose sensitive information of participants, leading to significant privacy concerns. Existing FL privacy-preserving techniques, such as differential privacy (DP) and secure multi-party computing (SMC), though offering viable solutions, face practical challenges including reduced performance and complex implementations. To overcome these hurdles, we propose a novel and pragmatic approach to privacy preservation in FL by employing localized federated updates (LF3PFL) aimed at enhancing the protection of participant data. Furthermore, this research refines the approach by incorporating cross-entropy optimization, carefully fine-tuning measurement, and improving information loss during the model training phase to enhance both model efficacy and data confidentiality. Our approach is theoretically supported and empirically validated through extensive simulations on three public datasets: CIFAR-10, Shakespeare, and MNIST. We evaluate its effectiveness by comparing training accuracy and privacy protection against state-of-the-art techniques. Our experiments, which involve five distinct local models (Simple-CNN, ModerateCNN, Lenet, VGG9, and Resnet18), provide a comprehensive assessment across a variety of scenarios. The results clearly demonstrate that LF3PFL not only maintains competitive training accuracies but also significantly improves privacy preservation, surpassing existing methods in practical applications. This balance between privacy and performance underscores the potential of localized federated updates as a key component in future FL privacy strategies, offering a scalable and effective solution to one of the most pressing challenges in FL. [ABSTRACT FROM AUTHOR]

In recent years, large digital companies have been gobbling up hundreds of smaller highly innovative firms involved in the collection and the processing of data. Some regulators, academics and practitioners have expressed concerns that Big Tech might use the increased market power and the greater concentration of consumers' personal data stemming from data-driven mergers to harm consumers in the form of lower privacy protection. They wonder whether and to what extent the EU Commission and/or national competition authorities should take into account data protection considerations when reviewing transactions under the Merger Regulation and/or national merger control rules. The Commission's decisional practice points towards three possible routes for integrating privacy concerns into competition analysis. Given their respective shortcomings, we explore the possibility of using the German Federal Cartel Office's line of reasoning in its Facebook decision as a model for the incorporation of privacy considerations into EU data-driven merger analysis. [ABSTRACT FROM AUTHOR]

The ability to gather and process large amounts of personal data is primarily held by a small group of companies. Some regulators, scholars, and practitioners have expressed concerns that major advertising-based platforms such as Google and Facebook may abuse their dominant position in the data market by requiring users to agree to the intensive collection of their data as a condition to the free access and use of those platforms’ core services. This raises the question of whether such broad collection of consumer data should be examined by European competition authorities as a foreclosure aiming at excluding current and potential competitors from the market of consumer data or as an excessive collection of personal data having the effect of lowering user’s privacy. This paper seeks to discuss this matter by examining the recent ruling in Meta by the European Court of Justice. [ABSTRACT FROM AUTHOR]

Lightning protection is important for weather radars to prevent critical damage or outages but can have negative effects on data quality. The existing lightning protection of the DWD polarimetric C-Band weather radar network consists of four vertical poles with a maximum diameter of 10 cm. During radar operation, these rods cause local scattering in the near field of the antenna, resulting in negative impacts on radar products. One effect is the removal of significant transmit power off the main beam axis and its addition to other areas or the side lobes. This results in wrongly localised precipitation fields in radial direction. The second effect is the loss of transmitted and received power, appearing as a decrease in system gain, and subsequently a underestimation of all power based radar moments in the vicinity of the rods. The underestimation in radar reflectivity Z then leads to a negative bias in the actual rain rate of approximately 20 % if a Z/R relationship is applied. These detrimental effects on data quality led to the requirement of developing a new lightning protection concept. The new concept must minimise the effect on data quality, but also provide sufficient protection from lightning strikes according to the existing regulations and requirements. Three possible lighting protection concepts are described in this paper: two using vertical rods of different diameters (16 and 40 mm, respectively) and one with horizontally placed rods outside the antenna aperture. Their possible influence on data quality is quantified through a dedicated measurement campaign by analysing resulting antenna patterns and precipitation sum products. Antenna patterns are analysed with respect to the side lobe levels compared to antenna patterns without lightning protection and the original lightning protection. With the newly tested lightning rods, the apparent side lobe levels are slightly increased compared to an antenna pattern taken without lightning protection, but are within the accepted antenna specifications. Compared to the original lightning protection, a decrease of up to -15 dB in apparent side lobe levels is found for all tested lightning protection options. Beam blockage is substantially reduced compared to the existing lightning protection as shown by the evaluation of QPE sums. These results and some structural considerations are a solid basis to recommend the installation of four rods with maximum 40 mm diameter for all 17 radar systems of the DWD weather radar network. [ABSTRACT FROM AUTHOR]

Middle-box based attacks create serious functional defects in the devices such as firewalls, address translators, load balancers, server units, and other data inspecting devices. Middle-box based attacks are more severe than terminal node-based attacks. These attacks are mainly initiated to malfunction the internal events of middle-level devices. Notably, Internet of Healthcare Things (IoHT) is a completely distributed and heterogeneous environment that leads into more vulnerable events. In the distributed medical system field, the middle-box devices manage secret medical data transactions, internal network communication, patient data protection principles and packet inspection procedures. The medical data collected from each patient needs secrecy and stability in various aspects in the IoHT environment. The middle-box based attacks injected into the IoHT nodes create inefficiency in maintaining patient data, Denial of Service (DoS) at middle-box nodes, excessive diagnosis time, and lack of data protection. For handling these issues, a new security architecture and a new attack detection model are proposed in this paper which has been developed using ranking subsets and Convolutional Neural Network (CNN) principles. The proposed CNN with ranking principles model is designed with binary class fuzzy fisher face optimization technique and flower pollination optimization method to initiate feature extraction. The extracted features of each data flow are analyzed using CNN and ranking subset methodologies. In this proposed model, the continuous involvement of middle-box events in respective devices are classified under various anomaly cases and legitimate cases. The proposed IoHT model attains maximum success rate than the existing models as indicated in implementation section. [ABSTRACT FROM AUTHOR]

Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi‐structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case‐by‐case analysis, as each instance will likely produce a different outcome. [ABSTRACT FROM AUTHOR]

This study aims to explore the application of computer simulation method based on light image processing in the monitoring of metabolic regulation data in exercise training. The study collected light image data during exercise training, including several parameters such as skin reflexes, heart rate response and skin temperature. Then, using computer simulation technology, the data were processed and analyzed to obtain metabolic regulation data during exercise. Finally, according to the analysis results, the training effect is evaluated and the training plan is adjusted. The results of the data analysis show that by monitoring the metabolic regulation data, the effect of exercise training can be more accurately evaluated and help adjust the training plan to achieve better training results. This method has the advantages of non-invasive, real-time and accurate, and is expected to be widely used in the field of sports training monitoring and regulation, and further improve the training effect and the formulation of individual training plans. [ABSTRACT FROM AUTHOR]

The document contains information about the process form book "Vorwerk", which includes explanations and samples for various court proceedings. It emphasizes that the book is indispensable for civil proceedings and provides 1,500 current samples for the entire process. The book is also equipped with a database access that provides the complete content and all included forms for download. Information is also provided about a specialized module for pharmaceutical law and a journal for health law. Further articles address topics such as billing fraud in the medical aids sector, the conflict between data protection and data usage in healthcare, and the joinder of parties in medical liability proceedings. The text contains information about various judgments and current developments in the field of medical liability law. It delves into the specifics of the joinder of parties in medical liability proceedings. Additionally, other topics such as the manipulation of blood values prior to the allocation of a donor organ, the admissibility of an expert opinion, and various judgments on medical liability are discussed. Current developments in medical education, hospital care, and nursing financing are also discussed. [Extracted from the article]

The article "Health Data Utilization Act: The Conflict between Data Protection and Data Usage" deals with the conflict between data protection and the use of health data. The law on improved use of health data in Germany aims to improve digital healthcare and research and create legal certainty. However, there are still challenges due to different nationwide regulations on health data usage in hospitals. Collaboration with data protection authorities and strengthening health data protection are also important factors. The article discusses the Health Data Utilization Act (GDNG) and the associated challenges in dealing with hospital data. It argues that the regulations for hospitals should be clearer, as the legal uncertainty is multiplied by the responsibility of around 100 institutions for data protection. It also addresses the differences between pseudonymization and anonymization of data and emphasizes the importance of a clear definition of legitimate interests for anonymization. The article also mentions the criminal prosecution of re-identification attempts and the need for further criminal procedural regulations to protect health data. It argues that the new regulation of § 25b SGB V exceeds medical competencies and could lead to discrimination. The possibility of objecting to data processing is also mentioned. It is suggested to conduct a pilot project with medical self-administration to verify the reliability of the data. The need for clear regulations for particularly sensitive data is emphasized. Overall, it is argued that data protection has been insufficiently considered in the implementation of the law. The article also addresses the risks to the protection goal of confidentiality in connection with criminal energy and artificial intelligence. It emphasizes that transparent and open collaboration with all stakeholders, as well as careful consideration of these risks, are necessary to achieve the desired health policy goal. The opt-out solution is proposed as a possible compromise to involve patients. Furthermore, the joinder of parties in medical liability proceedings is discussed as a means of securing subsequent claims. [Extracted from the article]

The article deals with billing fraud by medical supply providers in the healthcare sector and the legal consequences for violations of the prohibition on deposits, donations, and profit-sharing. There is disagreement about the interpretation of social law provisions, which leads to different outcomes in case law. The Federal Court of Justice affirms a damage in the amount of the billed sum if the service provider has no entitlement to remuneration. It is argued that the damage, despite possible savings for health insurance funds, should be considered a genuine and economic damage. The Health Data Utilization Act attempts to strike a balance between data protection and data usage. [Extracted from the article]

Blockchain has been gaining significant interest in several domains. However, this technology also raises relevant challenges, namely in terms of data protection. After the General Data Protection Regulation (GDPR) has been published by the European Union, companies worldwide changed the way they process personal data. This project provides a model and implementation of a blockchain system to store personal data complying with GDPR. We examine the advantages and challenges and evaluate the system. We use Hyperledger Fabric as blockchain, Interplanetary File System to store personal data off-chain, and a Django REST API to interact with both the blockchain and the distributed file system. Olympus has three possible types of users: Data Subjects, Data Processors and Data Controllers and a fourth participant, Supervisor Authority, that, despite not being an explicit role, can perform all verifications that GDPR mandates. We conclude that it is possible to create a system that overcomes the major challenges of storing personal data in a blockchain (Right to be Forgotten and Right to Rectification), while maintaining its desirable characteristics (auditability, verifiability, tamper resistance, distributed—remove single points of failure) and complying with GDPR. [ABSTRACT FROM AUTHOR]

The study of security and computer privacy has become a significant focus in security and privacy research. To reflect a website's, service's, or app's privacy policies, they're frequently used as a beginning step for researchers investigating the reliability of stated data regulations, user comprehension of policy, or user control methods. It's challenging to collect information about privacy practices from Internet resources like websites and mobile applications for analysis because of the wide variations in the structure, presentation, and content. Most computer privacy studies attempt to test new methods for detecting, classifying, and analyzing computer privacy content. However, numerous papers have been published to promote research activities, and no trace of any bibliometric analysis work on computer privacy demonstrates research trends. By conducting a thorough analysis of computer privacy studies, it searches the Scopus database, which contains over 2000 papers published between 1976 and 2020. Using the bibliometric analysis technique, this study examines research activity in Europe, South America, and other continents. This work investigated the number of papers published, citations, research area, keywords, institutions, topics, and researchers in detail. An overview of the research efforts is followed by listing the words into a classification of computer privacy analysis tools, emphasizing the significance of a computer privacy research study. According to the investigation findings, there are numerous significant implications of research efforts in Europe compared to other continents. Finally, we summarize the review findings for each part by highlighting potential future research directions. [ABSTRACT FROM AUTHOR]

The recently adopted Consumer Credit Directive incorporates new forms of credit and uses classic consumer protection instruments as well as introducing measures that increase control over lenders with the aim of preventing over-indebtedness. In general, more burdens are imposed on lenders and certain commercial practices are banned or restricted in all Member States. Moreover, the Directive is characterised by the fact that it includes consumer data protection and specifies a consumers' right not to be discriminated against, which may have some overlap with the General Data Protection Regulation and the on-going development of EU artificial intelligence legislation. [ABSTRACT FROM AUTHOR]

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function, a brand-new tristable locally active memristor model is first proposed in this paper. Here, a novel four-dimensional fractional-order memristive cellular neural network (FO-MCNN) model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance. Then, its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms. Subsequently, it is used toward secure communication application scenarios. Taking it as the pseudo-random number generator (PRNG), a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing (ASR-CS) model. Eventually, the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance. [ABSTRACT FROM AUTHOR]

Can the government buy its way around the Fourth Amendment's warrant requirement? As the panic over data-sharing after Dobbs illustrates, the answer is an urgent yes. Transactions between the government and data brokers--businesses that acquire, aggregate, and sell massive amounts of data on individuals' digital activities--fall outside the Stored Communications Act's hopelessly out-of-date guardrails and through a Fourth Amendment "loophole." Though the Supreme Court's 2018 Carpenter decision provides a useful framework for evaluating data's Fourth Amendment protection, it will not save us from data brokers. The "tick-tick-tock" cycle of Fourth Amendment precedent and privacy legislation is off As Orwell would say, the clocks are "striking thirteen." It is past time for Congress to pass new privacy legislation. But what should that legislation look like? History, as usual, offers clues. In this Note, I argue that to rebalance competing interests in light of paradigm technological change, Congress must learn from past mistakes in drafting the Stored Communications Act. I analyze three potential legislative solutions--the Fourth Amendment Is Not for Sale Act; the My Body, My Data Act: and California's "Delete Act"--and propose my own, which combines the strengths of each and provides flexibility for future technological developments. That way, when the Fourth Amendment clock strikes again, we will be ready. [ABSTRACT FROM AUTHOR]

The exponential growth of social networks has resulted in the generation of vast amounts of graph data containing sensitive information. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the attribute characteristics of social users, which results in a failure to safeguard potentially vulnerable private data effectively. To address these issues, an entropy-driven differential privacy protection scheme based on social graphlet attributes (EDP-SGA) is proposed. Firstly, a matrix-based algorithm is proposed for constructing an attribute intimacy matrix, which can quantify the strength of links among social users' attributes. Secondly, an influence algorithm based on user node attributes and information entropy is proposed, which can divide social networks into communities and select seed nodes. Thirdly, a privacy-preserving social network data publishing algorithm is proposed, which can combine graph modification techniques and differential privacy to convert sensitive graph data into an uncertain graph. Finally, experimental results demonstrate that the EDP-SGA can keep the balance between the privacy and the utility of social graph data. [ABSTRACT FROM AUTHOR]

With the growing significance of data privacy protection, Source‐Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true‐labeled knowledge for each category. To tackle this, Source‐Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one‐time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self‐adaptive Clustering‐based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self‐adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state‐of‐the‐art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost‐effective solution for SFADA, making it an attractive choice for real‐world applications where labeling resources are limited. [ABSTRACT FROM AUTHOR]

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested. [ABSTRACT FROM AUTHOR]

The analysis of aggregate, or marginal, data for contingency tables is an increasingly important area of statistics, applied sciences and the social sciences. This is largely due to confidentiality issues arising from the imposition of government and corporate protection and data collection methods. The availability of only aggregate data makes it difficult to draw conclusions about the association between categorical variables at the individual level. For data analysts, this issue is of growing concern, especially for those dealing with the aggregate analysis of a single 2 × 2 table or stratified 2 × 2 tables and lies in the field of ecological inference. As an alternative to ecological inference techniques, one may consider the aggregate association index (AAI) to obtain valuable information about the magnitude and direction of the association between two categorical variables of a single 2 × 2 table or stratified 2 × 2 tables given only the marginal totals. Conventionally, the AAI has been examined by considering p 11 —the proportion of the sample that lies in the (1, 1)th cell of a given 2 × 2 table. However, the AAI can be expanded for other association indices. Therefore, a new generalisation of the original AAI is given here by reformulating and expanding the index so that it incorporates any linear transformation of p 11 . This study shall consider the consistency of the AAI under the transformation by examining four classic association indices, namely the independence ratio, Pearson's ratio, standardised residual and adjusted standardised residual, although others may be incorporated into this general framework. We will show how these indices can be utilised to examine the strength and direction of association given only the marginal totals. Therefore, this work enhances our understanding of the AAI and establishes its links with common association indices. [ABSTRACT FROM AUTHOR]