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نتیجه جستجو - Data privacy

تعداد مقالات یافته شده: 50
ردیف عنوان نوع
1 Quantum Federated Learning With Decentralized Data
یادگیری فدرال کوانتومی با داده های غیرمتمرکز-2022
Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantumfederated learning(QFL).Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy onthe TensorFlowQuantum processor within variational quantumtensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
Index Terms: Quantum algorithm | quantum computing | quantum information | quantum machine learning.
مقاله انگلیسی
2 TUI Model for data privacy assessment in IoT networks
مدل TUI برای ارزیابی حریم خصوصی داده ها در شبکه های اینترنت اشیا-2022
The development of the Internet of Things (IoT) has been at the forefront of progressing societal functionality. However, the addition of IoT devices in conventional information technology (IT) infrastructure has raised and prioritized the concern of information security and data privacy. The Common Vulnerability Scoring System (CVSS) is a framework for providing information to the public about the impact of vulnerabilities and exploits executed on a multitude of devices. While the CVSS addresses a plethora of conditions for vulnerabilities, it does not adequately make end- users aware of the impact data privacy can have on their devices. The primary objective of this research work is to extend the existing CVSS and propose a new model that acknowledges Transparency, Unlinkability, and Intervenability (TUI) to address the data privacy issues of IoT devices when scoring impacts of vulnerabilities. Our research has developed this model to provide a new sufficient score for analyzing the true impact of compromised data privacy. After the development of the new scoring for TUI, our research highlights case studies to emphasize the impact our TUI model will have on the CVSS. We strongly believe that our proposed model benefit both the individual users (consumers of IoT devices) and the industry to portray the possible vulnerabilities from a user standpoint as well as a manufacturer standpoint.
keywords: حریم خصوصی داده ها | امنیت اینترنت اشیا | مدل سیا | امتیازدهی آسیب پذیری | امنیت دستگاه | ارزیابی امنیتی | Data privacy | IoT security | CIA model | Vulnerability scoring | Device security | Security assessment
مقاله انگلیسی
3 A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022
Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things (IoT) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article’s objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT
مقاله انگلیسی
4 Digital Livestock Farming
دامداری دیجیتال-2021
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
مقاله انگلیسی
5 Factors influencing effective use of big data: A research framework
عوامل مؤثر بر استفاده مؤثر از داده های بزرگ: چارچوب تحقیقی-2020
Information systems (IS) research has explored “effective use” in a variety of contexts. However, it is yet to specifically consider it in the context of the unique characteristics of big data. Yet, organizations have a high appetite for big data, and there is growing evidence that investments in big data solutions do not always lead to the derivation of intended value. Accordingly, there is a need for rigorous academic guidance on what factors enable effective use of big data. With this paper, we aim to guide IS researchers such that the expansion of the body of knowledge on the effective use of big data can proceed in a structured and systematic manner and can subsequently lead to empirically driven guidance for organizations. Namely, with this paper, we cast a wide net to understand and consolidate from literature the potential factors that can influence the effective use of big data, so they may be further studied. To do so, we first conduct a systematic literature review. Our review identifies 41 factors, which we categorize into 7 themes, namely data quality; data privacy and security and governance; perceived organizational benefit; process management; people aspects; systems, tools, and technologies; and organizational aspects. To explore the existence of these themes in practice, we then analyze 45 published case studies that document insights into how specific companies use big data successfully. Finally, we propose a framework for the study of effective use of big data as a basis for future research. Our contributions aim to guide researchers in establishing the relevance and relationships within the identified themes and factors and are a step toward developing a deeper understanding of effective use of big data.
Keywords: Big data | Effective use | Factors | Framework
مقاله انگلیسی
6 The ethics of AI in health care: A mapping review
اخلاق هوش مصنوعی در مراقبت های بهداشتی: یک بررسی نقشه برداری-2020
This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be ‘ethically mindful? A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/ nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review. We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.
Keywords: Artificial intelligence | Ethics | Healthcare | Health policies | Machine learning
مقاله انگلیسی
7 How far can Convention 108+ ‘globalise’? Prospects for Asian accessions
کنوانسیون 108+ تا چه اندازه می تواند جهانی شود؟ چشم انداز الحاق آسیایی-2020
The ‘globalisation’ of Council of Europe data protection Convention 108 through non-European accessions has continued steadily, with eight such accessions since the first in 2013. The ‘modernisation’ of the Convention was completed on 10 October 2018 when the amending protocol for the new ‘Convention 108+’ became open for signature. Any new countries from outside Europe wishing to accede will have to accede to both Convention 108 and the amending Protocol (ie to 108+). The standards required of the laws of acceding countries by 108+ are higher than those required by 108, and are arguably mid-way between 108 and those of the European Union’s General Data Protection Regulation (GDPR).
This article examines to what extent each of the 26 ‘countries’ (separate jurisdictions) in Asia are likely to be able to accede to 108+, if they wish to. As yet, none have acceded to 108. It proposes an efficient way to consider such a question across such a complex set of jurisdictions. Fifteen of the 26 Asian countries already have data privacy laws, and two others have official Bills for such laws. An assessment of the prospects for accession can be done by considering in order the following grounds which may be impediments to accession: Jurisdictions which are not States; States which are not democratic; Laws of inadequate scope; Laws lacking an independent data protection authority; Laws with substantive provisions falling short of 108+ ‘accession standards’; States with proposed Bills only; and States with no relevant laws or proposed Bills.
The most difficult step in this procedure is in deciding which of the substantive provisions of 108+ constitute its ‘accession standards’, or elements essential for accession to be invited. Neither the Convention, nor the guidelines issued by its Consultative Committee, shed much light on this question. However, previous practice under Convention 108, show there is some flexibility involved. The article concludes with suggestions as to how such flexibility can be made more transparent, and observations on which Asian countries, in light of the seven step assessment carried out in the article, are the most likely candidates to be able to accede to 108+, in both the short and medium terms.
مقاله انگلیسی
8 Mapping the development of China’s data protection law: Major actors, core values, and shifting power relations
ترسیم توسعه قانون حفاظت از داده های چین: بازیگران اصلی، ارزش های اصلی و تغییر روابط قدرت-2020
This Article seeks to map the possible paths of the development of China’s data protection law by examining the changing power relations among three major actors - the State, digital enterprises and the public in the context of China’s booming data-driven economy. We argue that focusing on different core values, these three major actors are the key driving forces shaping China’s data protection regime. Their dynamic and multidimensional power relations have been casting the development of China’s data protection law with various uncertainties. When persuing different, yet not always conflicting values, these three major actors may both cooperate and compete with each other. Based on our careful analysis of the shifting power relations, we identify and assess three possible paths of the development of China’s data protection law. We are much concerned that the proposed comprehensive data protection law might be a new attempt of the State to win legitimacy abroad, while actually trying to reinforce massive surveillance besides economic goals. We argue that a modest alternative may be that this law might show some genuine efforts for protecting data privacy, but still with poor enforcement. Last, we argue that the most desirable development would be that this law could provide basic but meaningful and effective protection for data privacy, and lay a good foundation for further development.© 2020 Bo Zhao and Yang Feng. Published by Elsevier Ltd. All rights reserved.
مقاله انگلیسی
9 Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
فدراسیون دانش: یک چارچوب متحد و سلسله مراتبی حفظ حریم خصوصی هوش مصنوعی-2020
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many missioncritical and data-sensitive scenarios, such as finance, government, and health. In the meantime, tremendous datasets are scattered in isolated silos in various industries, organizations, different units of an organization, or different branches of an international organization. These valuable data resources are well underused. To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation - KF) to address these challenges by enabling AI while preserving data privacy and ownership. Beyond the concepts of federated learning and secure multi-party computation, KF consists of four levels of federation: (1) information level, low-level statistics and computation of data, meeting the requirements of simple queries, searching and simplistic operators; (2) model level, supporting training, learning, and inference; (3) cognition level, enabling abstract feature representation at various levels of abstractions and contexts; (4) knowledge level, fusing knowledge discovery, representation, and reasoning. We further clarify the relationship and differentiation between knowledge federation and other related research areas. We have developed a reference implementation of KF, called iBond Platform, to offer a productionquality KF platform to enable industrial applications in finance, insurance, marketing, and government. The iBond platform will also help establish the KF community and a comprehensive ecosystem and usher in a novel paradigm shift towards secure, privacy-preserving and responsible AI. As far as we know, knowledge federation is the first hierarchical and unified framework for secure multi-party computing (statistics, queries, searching, and low-level operations) and learning (training, representation, discovery, inference, and reasoning).
Index Terms: Knowledge Federation |Knowledge | Federated Learning | Secure Multi-party Computation | Secure Multi-party Learning
مقاله انگلیسی
10 Smartphone platforms as privacy regulators
پلتفرم های گوشی های هوشمند به عنوان تنظیم کننده حریم خصوصی-2020
A series of recent developments highlight the increasingly important role of online platforms in impacting data privacy in today’s digital economy. Revelations and parliamentary hearings about privacy violations in Facebook’s app and service partner ecosystem, EU Court of Justice judgments on joint responsibility of platforms and platform users, and the rise of smartphone app ecosystems where app behaviour is governed by app distribution platforms and operating systems, all show that platform policies can make or break the enjoyment of privacy by users. In this article, we examine these developments and explore the question of what can and should be the role of platforms in protecting data privacy of their users. The article first distinguishes the different roles that platforms can have in ensuring respect for data privacy in relevant ecosystems. These roles include governing access to data, design of relevant interfaces and privacy mechanisms, setting of legal and technical standards, policing behaviour of the platform’s (business) users, coordinating responsibility for privacy issues between platform users and the platform, and direct and indirect enforcement of a platform’s data privacy standards on relevant players. At a higher level, platforms can also perform a role by translating different international regulatory requirements into platform policies, thereby facilitating compliance of apps in different regulatory environments. And in all of this, platforms are striking a balance between ensuring the respect for data privacy in data-driven environments on the one hand and optimization of the value and business opportunities connected to the platform and underlying data for users of the platform on the other hand.
After this analysis of platforms’ roles in protecting privacy, the article turns to the question of what should this role be and how to better integrate platforms in the current legal frameworks for data privacy in Europe and the US. The article will argue for a compromise between direct regulation of platforms and mere self-regulation, in arguing that platforms should be required to make official disclosures about their privacy-related policies and practices for their respective ecosystems. These disclosures should include statements about relevant conditions for access to data and the platform, the platform’s standards with respect to privacy and the way in which these standards ensure or facilitate compliance with existing legal frameworks by platform users, and statements with respect to the risks of abuse of different data sources and platform tools and actions taken to prevent or police such abuses. We argue that such integration of platforms in current regulatory frameworks is both feasible and desirable. It would make the role that platforms already have in practice more explicit. This would help to highlight best practices, create more accountability and could save significant regulatory and compliance resources in bringing relevant information together in one place. In addition, it could provide clarity for business users of platforms, who are now sometimes confronted with restrictive decisions by platforms in ways that lack transparency and oversight.
Keywords: Online platforms | Smartphones | Data protection | Privacy | Regulation | Disclosures
مقاله انگلیسی
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