ABSTRACT
Data leakage is a case that often occurs anywhere. Indonesia is one of the countries with the most population that is currently having data leakage cases. The leak of data on the COVID-19 PeduliLindung tracking application, triggered a public reaction because it was considered dangerous. Based on this, the aim of the study is to predict the sentiment pattern using Naïve Bayes. This study is important to do sentiment analysis to find out the public's reaction, it can become a recommendation in developing applications that are safer in data storage. The experiment in this study used data from Twitter which was taken for 14 days, between 16-21 May 2022. The data was processed using Google Collab and the Naïve Bayes approach. The experimental results are that negative sentiment is greater than positive sentiment and neutral sentiment, which is 93%. While the accuracy of positive sentiment is 81% and Neutra sentiment is 90%. This means the leak of public data from a COVID-19 tracing application in Indonesia has a greater negative sentiment. The difference in the study is the data testing process was carried out five times to get good accuracy from the model. And the results show that Naïve Bayes is a model that is quite recommended for prediction of sentiment patterns. © 2022 IEEE.
ABSTRACT
In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.
ABSTRACT
As the COVID-19 continues to spread globally, more and more companies are transforming into remote online offices, leading to the expansion of electronic signatures. However, the existing electronic signatures platform has the problem of data-centered management. The system is subject to data loss, tampering, and leakage when an attack from outside or inside occurs. In response to the above problems, this paper designs an electronic signature solution and implements a prototype system based on the consortium blockchain. The solution divides the contract signing process into four states: contract upload, initiation signing, verification signing, and confirm signing. The signing process is mapped with the blockchain-linked data. Users initiate the signature transaction by signing the uploaded contract's hash. The sign state transition is triggered when the transaction is uploaded to the blockchain under the consensus mechanism and the smart contract control, which effectively ensures the integrity of the electronic contract and the non-repudiation of the electronic signature. Finally, the blockchain performance test shows that the system can be applied to the business scenario of contract signing. © 2022 IEEE.
ABSTRACT
OBJECTIVE: The burden of data entry in public platforms used for reporting patients with novel coronavirus disease 2019 (COVID-19) is a challenge in the healthcare setting. The key to mitigating the burden of data entry is system integration and elimination of double data entry. In addition, the linkage between public platforms and electronic medical records (EMRs) involves external networks, which are an important target for security management. The purpose of this study was to elucidate the status and challenges of infrastructure for continuous data reporting from hospitals in Japan. MATERIALS AND METHODS: An online survey of Japanese care delivery institutions was conducted from January 25 to February 22, 2021, to obtain data on the admission of patients with COVID-19, use of information infrastructures, and status of network connections with external organizations. The survey request was distributed to each care delivery institution by Japanese health authorities. RESULTS: Of the care delivery institutions that responded to the survey, 53.9% treated patients with COVID-19. Of these institutions, 73.3% used EMRs. 57.8% of the EMRs were connected to an external network. The purpose of connecting to the external network was to contribute to regional health information-sharing with other hospitals (22.0%), report online medical insurance claims (27.5%), and conduct intrahospital system maintenance (61.5%). A frequent concern about connecting an EMR to an external network was data leakage. DISCUSSION: In cases where the frequency of reporting patients with COVID-19 is high, health authorities should provide information regarding anti-data-leakage measures and coordinate frameworks for efficient, sustainable data collection. CONCLUSIONS: We obtained information on existing infrastructures for patient data sharing among care delivery institutions and public health authorities. Our findings may be referenced by the government to make informed decisions about investments.
ABSTRACT
While the digital information age has brought us convenience in recent years. It has also brought many security risks. The centralized data storage poses the risk of data leakage. With the breakout of the Novel Coronavirus, it is common for schools to import the health management tools such as the body temperature monitoring system. If these health data are integrated together, it can facilitate school authorities to track students' health status, but centralized management has the risk of widespread data leakage. We propose a decentralized, personal cloud data model. The history of a student's health data will be stored only in each student's personal online datastore managed by Edu Pod, which is a student health information data management application that provides various services and mechanisms to secure the personal data in the Pod. By decentralizing the data to the students' individual Pods, it prevents the occurrence of large-scale data leaks by centralized management. We apply this model to the Campus Health Information System (CHIS) which we are studying in. The student's temperature data collected by face recognition will eventually be stored only in his or her personal Pod, which is a portable educational information storage unit, and the student can share the data with the university and any third-party health management software. The application of a centralized personal cloud data model can help students' personal data not be limited to a centralized server farm and can control their personal data more freely. It can also avoid information leakage and help to protect personal privacy at the same time. © 2021 IEEE.