High Level Of Privacy Research Articles

Indentity Management System Using Blockchain and Survey

Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity

Personalized Federated Graph Learning on Non-IID Electronic Health Records.

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.

Differential privacy Kalman filtering for graphical dynamic systems: Performance improvement and privacy calibration

This paper investigates differential privacy Kalman filtering for graphical dynamic systems. An enhanced differential privacy Kalman filter is initially developed by leveraging the topology, a unique characteristic of graphical dynamic systems, to improve the filtering performance. And then upper and lower error bounds of the enhanced filter are established to evaluate the recipient's ability to infer sensitive data. Based on these error bounds, a privacy calibration guideline is provided to balance privacy and usability of sensitive data. A notable feature of the enhanced filter is that its Kalman gain matrix is a diagonal matrix, which facilitates the independent update of information at each node by reducing the coupling between nodes. This feature diminishes the accumulation of filtering errors, thereby improving the data availability for the recipient. Finally, the effectiveness of the proposed approach is verified through a simulation of epidemiological data analysis, and the results show that the enhanced filter performs well in systems with high privacy levels.

Novel competitive fractional chaotic circuits interaction networks with hierarchical structure and encryption application

In the realm of complex networks, the challenge of ensuring secure communication amidst the vulnerabilities of conventional encryption methods has become increasingly critical. This study delves into the complex realm of synchronized behaviors in networks, employing fractional-order chaotic circuits within hierarchically structured competitive interaction networks to enhance encryption security, particularly for medical image transmission. We propose a novel paradigm that transcends traditional synchronization methods used across various disciplines, from engineering to social sciences, by unveiling the intricate dynamics of how units within networks share interactions. Our approach leverages the unique properties of fractional chaos and network hierarchy, demonstrating that the proposed model, characterized by multi-directed links and competitive strategies, significantly improves synchronization. Through detailed analysis, including bifurcation diagrams and Lyapunov exponent plots, we uncover the optimal configurations of coupling strength and fractional order that lead to enhanced network synchronization. This synchronization is pivotal for our encryption application, showcasing a high level of security and privacy in the transmission of medical images. The encryption technique benefits from the network’s complex and synchronized dynamics, rendering it a formidable challenge for potential attackers to decipher the encrypted data. While our findings offer a promising mechanism for creating robust communication networks capable of securing sensitive medical data, the implications of our work extend beyond this application. The successful application of fractional-order chaotic circuits sets a groundwork for securing diverse types of data transmissions against the evolving landscape of cyber threats. This research not only marks a significant advancement in network security but also opens new avenues for applying these principles across a spectrum of fields where data security and privacy are paramount.

The Costs of Anonymization: Case Study Using Clinical Data.

Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice. The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study. The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results. Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy. Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data. German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971. RR2-10.1093/ndt/gfr456.

Arrive and retreat: London’s ultra-prime apartment blocks as dark urban renaissance

The expansion of an archipelago of residential blocks across London’s West End has created an urban renaissance of sorts, but it is a rebirth that has targeted the expanded ranks of the global super-rich, yielding a visibly luxurious, often vacant residential remaking, rather than the aspirations for a diverse, vibrant and open urbanism envisaged some years ago for the city. New luxury apartment blocks offer high levels of privacy, security and the seamless integration of bodies and vehicles into underground or enclosed entry points. These ‘ultraland’ blocks are less places of conviviality and encounter, offering instead a kind of dark space urbanism that facilitates a partial social engagement with local environs by barely present owners. Combining an analysis of the planning applications for these developments with extensive street observation in London’s West End we discuss the social and spatial integration of these developments. We argue that a key effect is one of social ‘reduction’—the sense of null spaces that absorb bodies, furnishings and cars in ways that are the antithesis of earlier ambitions for socially vital and democratic urban spaces. We discuss the significance of these changes for contemporary life in the city more broadly.

Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain

The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.

Long-Acting Reversible Etonogestrel Subdermal Implant in Adolescents.

Several effective contraceptive options are available for use by adolescents, including the long-acting reversible subdermal implant and intrauterine devices, which provide a high level of convenience, privacy, and effectiveness for an adolescent. Knowledge of all the effective birth control methods is essential for the pediatrician to be able to provide effective contraceptive counseling for an adolescent. An approach to counseling using a reproductive justice framework, which allows the provider and adolescent patient to engage in shared decision-making, is described. This article focuses on the long-acting reversible etonogestrel (ENG) subdermal implant for adolescents. The ENG implant is labeled for preventing pregnancy by suppressing ovulation. The ENG implant may also have a role in ameliorating dysmenorrhea and heavy menstrual bleeding. Postlabeling studies indicate that the ENG implant is effective for up to 5 years, although the device's labeling states effectivenessup to 3 years. The main contraindication to using the ENG implant is pregnancy itself. Safe initiation of the ENG implant is described, including an approach to determine whether an adolescent is pregnant. The main adverse effect of the ENG implant is an unpredictable bleeding pattern that is most often ameliorated by use of nonsteroidal anti-inflammatory medications, as well as estrogen, if not contraindicated for the patient. Details of the insertion and removal procedures, including potential complications, are described to enable the pediatrician to provide effective anticipatory guidance for the adolescent.

PRIVIC: A privacy-preserving method for incremental collection of location data

With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability (geo-ind). However, geo-ind alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of service. We then proceed to study the utility of BA in terms of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we de-noise the reported data by applying the iterative Bayesian update (IBU), an instance of the expectation-maximization method. It turns out that BA and IBU are dual to each other, and as a result, they work well together, in the sense that the statistical utility of BA is quite good and better than LAP for high privacy levels. Exploiting these properties of BA and IBU, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. We illustrate the soundness and functionality of our method both analytically and with experiments.

Student Perceptions of the Effectiveness of the Scola Learning Management System (LMS) in Improving the Quality of Learning

This study aims to explore students' perceptions of the Teaching and Learning Process (Kegiatan Belajar Mengajar - KBM) involving the use of Learning Management System (LMS), particularly Scola. The research method employed is qualitative, supported by a descriptive quantitative approach, combining observations, literature reviews, and questionnaires as data collection tools. The results of the study indicate a high interest among students in using LMS Scola to support the teaching and learning process. Students express satisfaction with the use of LMS Scola, considering it effective, efficient, user-friendly, and ensuring a high level of data privacy. These findings indicate that the utilization of technology, especially LMS, can have a positive impact on the learning process, motivate students, and create a more interactive learning environment. The implications of this research provide a crucial push to encourage the adoption of LMS, such as Scola, in various educational institutions. By leveraging this technology, it is anticipated that the learning process can become more efficient, effective, and supportive of overall educational development. This study serves as a robust foundation for enhancing the quality of education through the integration of technology in the Teaching and Learning Process (KBM).

People’s political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

BackgroundAs the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people’s residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people’s concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people’s private locations.MethodsWe conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people’s political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables.ResultsWe observed a tradeoff relationship between people’s privacy concerns and the acceptance (and perceived social benefits) of the control measures. People’s perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns.ConclusionsOur results first suggest that people’s privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people’s acceptance and perceived social benefits of the control measures but may not reduce people’s privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people’s privacy concerns tend to increase with their conservatism and individualism.

Forward Secrecy Attack on Privacy Preserving Machine Authenticated Key Agreement for Internet of Things

Internet of things (IoT) is to connect billions of devices and machines via Internet and to have a smart system. Sensors and devices in IoT environment are connected and communicated together. Connecting such a huge number of devices requires high level of security and privacy. A crucial characteristic of ubiquitous IoT devices is their limited resources. In recent times, a scheme for privacy-preserving machine authenticated key agreement scheme (PPMAKA) has been introduced for the IoT environment. It was argued that PPMAKA provides security and privacy at the same time including forward secrecy. Nevertheless, this paper will demonstrate that PPMAKA lacks forward secrecy, a crucial security and privacy feature in the IoT environment. We use Cannetti and Krawzyck threat model for the detailed analysis of PPMAKA. Furthermore, we provide remarks for the future research as it is recommendable to design any security and privacy schemes over IoT environments with lightweight operation and communication property, authenticated key agreement with forward secrecy, anonymity and unlinkability.

A novel deep-learning based approach to DNS over HTTPS network traffic detection

<div align="center"><span lang="EN-US">Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a <a name="_Hlk143263730"></a>DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.</span></div>

Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification

AbstractCorrect identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.

Security in wireless body area networks via anonymous authentication: Comprehensive literature review, scheme classification, and future challenges

Authentication is a security practice that seeks to validate a user identity to various entities, yet doing so across a wireless network poses inherent risks. Wireless Body Area Networks (WBANs) are a unique form of network that contains e-healthcare patient data, thus WBANs require a high level of privacy and security. There are several anonymous WBAN authentication systems in the literature, but this is the first paper to conduct a complete review of all the various schemes, then tabulate and compare the results. Doing so reveals a number of research avenues that need either greater depth, or systems development. This article presents a thorough taxonomy and survey of the anonymous authentication methods, illustrating the security services and vulnerabilities, and then the properties of an ideal anonymous authentication scheme. The various schemes reviewed are also classified by their main encryption algorithm, such as bilinear parings-based schemes, elliptic curve cryptography-based solutions, lattice-based methods, and XOR-based approaches. The main authentication capabilities, cryptographic features, security advantages, evaluation metrics, and shortcomings are all specified and discussed. A thorough comparison of anonymous authentication methods is presented to demonstrate each scheme's resistance to a variety of security threats. The study concludes that very few schemes have addressed the group authentication problem, which is very important in healthcare systems. Also, in the multi-factor authentication context, which provides a higher level of security, only 11% of the schemes used three factors, and 1% of them utilized four factors. Besides, DDoS attacks, as a growing concern in all computing environments, are handled by a narrow number of schemes. At last, a set of recommendations for further research based on identified literature gaps.

Techno-legal expertise and the datafication of the state: Big data, accountability and the value of a social license with institutional roots

As governments around the world become increasingly datafied, debates are emerging about the best ways to attend to the complex socio-political implications of big data and the datafication of the state. Drawing on semi-structured interviews with senior executives and data experts within Australian government agencies, high-level privacy experts, and other experts in public sector data integration, this article examines how a sociotechnical imaginary about data-driven, democratic government acts within and alongside routinely bureaucratised forms of techno-legal risk management to inform the work of Australia’s data integration experts. Notably, these techno-legal experts recognised the limitations of techno-legal data management, and mobilised notions of the social license when seeking to (re)orient the trajectories of data integration towards the democratic, data-driven government they envisage. Contributing to debates about the datafication of the state, we argue that while a social license will not be a panacea to all the complexities of datafication, a social license with institutional roots is essential to deepen accountability towards publics, and to help ensure that datafication can be co-produced by, and reflective of, the sociotechnical futures envisaged by a broader range of publics.

Influences of Ethical Values of e-Travel Agents on Authenticity and Trust Formations: The Moderating Role of Privacy Concerns

While ethical values and privacy concerns are considered factors that influence online consumers’ decision-making process, few studies have examined the interaction effect between the two factors in the online brand development process. Therefore, this study proposed a research model to maximize the effectiveness of e-travel agents’ ethical business on brand loyalty with a consideration of the roles of authenticity and two dimensions of trust. To do so, this study collected data from 405 online customers of e-travel agents. The empirical results indicated that consumers’ perception of an e-travel agent’s ethical values leads to higher levels of authenticity, trust, and brand loyalty. However, the empirical findings also addressed that the impact of authenticity on cognitive trust became significantly weaker under the high level of online privacy concerns. A deep understanding of the interaction between authenticity/brand trust and online privacy concerns is critical in the online brand development context.

MQTT-A: A Broker-Bridging P2P Architecture to Achieve Anonymity in MQTT

The demand for privacy in the current digital era is continuously growing. This is particularly true in the context of IoT, in which huge amounts of data are handled. Communication anonymity is a fundamental requirement when high privacy levels should be guaranteed. On the other hand, very little attention has been devoted to this problem in the past scientific literature, when referring to MQTT, which is the de-facto standard for IoT communication. In this paper, we try to cover this gap. Specifically, we propose a new protocol, called MQTT-A, which extends the MQTT bridging mechanism to support the anonymity of both publishers and subscribers. This task is accomplished through the P2P collaboration of intermediate bridge brokers, which forward the requests of clients so that the final broker cannot understand the actual source/destination. Moreover, an anonymity-preserving topic discovery mechanism is provided, which allows clients to discover available topics and associated brokers, preventing client identification. Importantly, all the MQTT-A messages are exchanged by leveraging standard MQTT primitives and the bridging mechanism natively offered by MQTT. This allows us not to require changes in the standard MQTT infrastructure. To validate the performance of our solution, we performed a deep experimental campaign by deploying the bridge brokers on cloud platforms in various countries of the world. The experimental validation shows that, the price of latency we have to pay because of the trade-off with anonymity is quite reasonable. Moreover, no significant impact on goodput occurs in the case of good network conditions.

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.

Quality of Care is Perceived to be High with Community-based Antiretroviral Therapy (ART) Services for Female Sex Workers in Tanzania: Qualitative Findings from a Pilot Implementation Science Study.

This qualitative study reports on female sex workers' (FSWs) perceptions of the quality of antiretroviral therapy (ART) services they received as part of a community-based ART distribution intervention compared to services received by FSWs in the standard of care (SOC) arm. In-depth interviews were conducted with 24 participants to explore their perceptions of the quality of ART services. Data was analyzed using a quality-of-care framework that included but was not limited to, domains of accessibility, effective organization of care, package of services, and patient-centered care. Overall, FSWs in the intervention arm reported community-based ART services to be highly accessible, organized, and effective, and they highly valued the patient-centered care and high level of privacy. Community-based ART programs for FSWs can have high quality-of-care, which can have a positive effect on HIV treatment outcomes for FSWs.