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Register a new userBalancing Personal Privacy and Public Safety during COVID-19: The Case of South Korea
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Na-Young Ahn
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Koreas approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.
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Without proper medication and vaccination for the COVID-19, many governments are using automated digital healthcare surveillance system to prevent and control the spread. There is not enough literature explaining the concerns and privacy issues; hence, we have briefly explained the topics in this paper. We focused on digital healthcare surveillance systems privacy concerns and different segments. Further research studies should be conducted in different sectors. This paper provides an overview based on the published articles, which are not focusing on the privacy issues that much. Artificial intelligence and 5G networks combine the advanced digital healthcare surveillance system; whereas Bluetooth-based contact tracing systems have fewer privacy concerns. More studies are required to find the appropriate digital healthcare surveillance system, which would be ideal for monitoring, controlling, and predicting the COVID-19 trajectory.
How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus such as contact tracing, clusters identification, movement restrictions, and status validation. Specifically, we classify available techniques based on some characteristics such as technology, architecture, trade-offs (privacy vs utility), and the phase of adoption. We present a novel approach for evaluating privacy using both qualitative and quantitative measures of privacy-utility assessment of contact tracing applications. In this new method, we classify utility at three (3) distinct levels: no privacy, 100% privacy, and at k where k is set by the system providing the utility or privacy.
From global pandemics to geopolitical turmoil, leaders in logistics, product allocation, procurement and operations are facing increasing difficulty with safeguarding their organizations against supply chain vulnerabilities. It is recommended to opt for forecasting against trending based benchmark because auditing a future forecast puts more focus on seasonality. The forecasting models provide with end-to-end, real time oversight of the entire supply chain, while utilizing predictive analytics and artificial intelligence to identify potential disruptions before they occur. By combining internal and external data points, coming up with an AI-enabled modelling engine can greatly reduce risk by helping retail companies proactively respond to supply and demand variability. This research paper puts focus on creating an ingenious way to tackle the impact of COVID19 on Supply chain, product allocation, trending and seasonality. Key words: Supply chain, covid-19, forecasting, coronavirus, manufacturing, seasonality, trending, retail.
The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increased complexity creates new safety, security, privacy, and usability challenges far beyond the difficult challenges individuals face just securing a single device. We highlight some of the negative trends that smart devices and collections of devices cause and we argue that issues related to security, physical safety, privacy, and usability are tightly interconnected and solutions that address all four simultaneously are needed. Tight safety and security standards for individual devices based on existing technology are needed. Likewise research that determines the best way for individuals to confidently manage collections of devices must guide the future deployments of such systems.
Human brain activity collected in the form of Electroencephalography (EEG), even with low number of sensors, is an extremely rich signal. Traces collected from multiple channels and with high sampling rates capture many important aspects of participants brain activity and can be used as a unique personal identifier. The motivation for sharing EEG signals is significant, as a mean to understand the relation between brain activity and well-being, or for communication with medical services. As the equipment for such data collection becomes more available and widely used, the opportunities for using the data are growing; at the same time however inherent privacy risks are mounting. The same raw EEG signal can be used for example to diagnose mental diseases, find traces of epilepsy, and decode personality traits. The current practice of the informed consent of the participants for the use of the data either prevents reuse of the raw signal or does not truly respect participants right to privacy by reusing the same raw data for purposes much different than originally consented to. Here we propose an integration of a personal neuroinformatics system, Smartphone Brain Scanner, with a general privacy framework openPDS. We show how raw high-dimensionality data can be collected on a mobile device, uploaded to a server, and subsequently operated on and accessed by applications or researchers, without disclosing the raw signal. Those extracted features of the raw signal, called answers, are of significantly lower-dimensionality, and provide the full utility of the data in given context, without the risk of disclosing sensitive raw signal. Such architecture significantly mitigates a very serious privacy risk related to raw EEG recordings floating around and being used and reused for various purposes.
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