دانلود مقاله اشتراک داده ها و مسائل حریم خصوصی
چکیده
مقدمه
روند تسریع تحقیقات کووید 19
داده های پزشکی و اشتراک گذاری
برنامه های کووید 19 و حریم خصوصی
بحث و پیشنهاد برای تحقیقات بیشتر
منابع
Abstract
Introduction
The process of accelerating COVID-19 research
Medical data and sharing
COVID-19 applications and privacy
Discussion and suggestions for further research
References
چکیده
بیماری کروناویروس 2019 (COVID-19) (2019-nCov) که برای اولین بار در دسامبر 2019 در ووهان/چین شناسایی شد و در مدت کوتاهی به کل جهان سرایت کرد، در فوریه توسط سازمان بهداشت جهانی به عنوان یک کروناویروس جدید توضیح داده شد. 11، 2020. کشورها در حال توسعه استراتژی های مختلف در برابر گسترش تهدید همه گیر هستند. اصلی ترین آنها توسعه برنامه های مبتنی بر وب یا تلفن همراه برای کاهش گسترش و آسیب اقتصادی همه گیری با استفاده از مجموعه داده های COVID-19 است. مشاهده می شود که برنامه های کاربردی موجود توسعه یافته در چارچوب این انتظارات حاوی اطلاعات مکان مطلق (مستقیم)، اطلاعات مکان نسبی (غیر مستقیم) و داده های مشخصه ای هستند که افراد را تعریف می کنند. حتی اگر این داده‌ها برای مبارزه جهان با کووید-19 اهمیت زیادی داشته باشد، باید خطراتی را که ممکن است پس از همه‌گیری رخ دهد، زمانی که روابط اطلاعات در نظر گرفته می‌شود، پیش‌بینی کرد. به منظور اندازه گیری خطر حریم خصوصی این نوع برنامه های کاربردی حاوی داده های شخصی، معیارهای حریم خصوصی در ادبیات تعریف شده است. در این فصل، ما دیدگاهی در مورد به اشتراک گذاری و حفظ حریم خصوصی داده های پزشکی در محدوده COVID-19 ارائه می دهیم. در این زمینه، مدل‌های حریم خصوصی، معیارها و رویکردهای انتخاب مدل مناسب، به‌ویژه برای برنامه‌های COVID-19 توضیح داده می‌شوند، و ما همچنین یک معیار جدید با رویکرد آنتروپی به معیارهای تعریف شده در ادبیات و موثر در تعیین حریم خصوصی پیشنهاد می‌کنیم.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The coronavirus disease 2019 (COVID-19) (2019-nCov), which was first detected in Wuhan/China in December 2019 and spread to the whole world in a short time, was explained as a new coronavirus by the World Health Organization on February 11, 2020. Countries are developing various strategies against the spread of epidemic threat. The main ones are to develop web-based or mobile applications to reduce the spread and economic damage of the epidemic by making use of COVID-19 datasets. It is seen that the existing applications developed within the framework of these expectations contain absolute location information (direct), relative location information (indirect), and characteristic data defining people. Even if these data mean a lot to the world's struggle with COVID-19, it is necessary to foresee the risks that may occur after the epidemic when the relations of the information are considered. In order to measure the privacy risk of this kind of applications containing personal data, privacy metrics have been defined in the literature. In this chapter, we give a perspective about the sharing and privacy of medical data within the scope of COVID-19. Within this context, privacy models, metrics, and approaches for selecting the appropriate model are described, in particular for COVID-19 applications, and we also propose a new metric with the entropy approach to metrics defined in the literature and effective in determining the privacy score.
Introduction
As of February 11, 2020, the coronavirus disease 2019 (COVID-19) (2019-nCov) virus, which was declared as the new coronavirus by the World Health Organization (WHO), has spread to the whole world in a short time [1]. Coronaviruses are pleomorphic RNA viruses typically containing crown-shaped peplomers that are 80–160 nM in size and 27–32 kb positive polarity [2]. Coronaviruses are zoonotic pathogens that are present in humans and various animals, with their high mutation ratio. The coronavirus infection presents with a broad range of clinical features from asymptomatic course to requirement of hospitalization in the intensive-care unit [2]. This virus often causes serious complications with acute respiratory problems and causes death. According to WHO's Situation report, the COVID-19 virus continues to spread around the world and 2,719,897 confirmed cases were documented via case reporting forms received from 113 countries as of April 25 [1]. There is a large literature examining the adaptation of the virus to natural changes, its biological structure, its spreading and contagion structure, and prevention methods. Countries are developing various strategies against the epidemic threat. The main ones are to increase social distance, to provide support products to health units by using technology, and to develop applications to reduce the spread and economic damage of the epidemic by making use of data. Many countries aim to achieve fast and efficient results by collaborating on project calls for vaccine and drug development. There is a mobilization of physical measures taken to slow the spread of the outbreak as well. Masks are produced voluntarily using the 3D printer technology. There are also studies on the course of the epidemic and human behavior by following social media flows. By making use of radiologic images, early diagnostic studies supported by artificial intelligence feed the literature day by day. Using online tools more effectively has become widespread for patient assistance. The world is fighting against the epidemic and its effects by using big data and related technology effectively. Many researchers are working on COVID-19 datasets, and also some specific web or mobile applications related to coronavirus all over the world has begun coming to the front.
Results and analyses
In this study, we propose a new privacy metric approach by presenting a perspective on the sharing and privacy medical data within the scope of COVID-19. Many researchers have been working on COVID-19 datasets and developing web or mobile applications aiming to control the spread of coronavirus all over the world. The developed products need to keep absolute position, relative position, and some personal data in order to meet the expectation. This process, which operates fast due to a vital situation, brings with it some risks of privacy. Developers use various methods to protect privacy in their products, which are called as PETs. To evaluate the effectiveness of a PET, privacy criteria are needed that can measure the level of privacy. A privacy metric is calculated by taking features of a system as input, such as the number of users who have indistinguishable characteristics or the quantity of sensitive information. At the end of calculation a numeric value is obtained that provides the privacy level. This value also can be used as a comparative parameter for other PETs. This study proposes a new privacy metric with the Shannon entropy approach called privacy cost using metrics defined in the literature [27]. Entropy is also a privacy metric, which is the base for many other metrics, that measures uncertainty. Therefore the expression of privacy cost demonstrates that the low entropy value means high privacy risk.