Background
On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus’ wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users’ information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users’ privacy.
Methodology
This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen.
Results
This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users’ privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations.
Conclusions
Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.
With the advances in Internet technologies and services, social media has been gained extreme popularity, especially because these technologies provide potential anonymity, which in turn harbors hacker discussion forums, underground markets, dark web, and so on. Internet relay chat (IRC) is a real-time communication protocol actively used by cybercriminals for hacking, cracking, and carding. Hence, it is particularly urgent to identify the authors of threat messages and malicious activities in IRC. Unfortunately, author identification studies in IRC remain as an underexplored area. In this research, we perform novel IRC text feature extraction methods and propose the first author attribution version of the deep forest (DF) model that is an ensemble of ensembles that utilizes the fusion of ensemble learning techniques. Our approach is supported by autonomic IRC monitoring. Experiments show that our approach is highly effective for author attribution and attains high accuracy even when the number of candidates is large while training data is limited.
Presently, the popularity of cloud computing is gradually increasing day by day. The purpose of this research was to enhance the security of the cloud using techniques such as data mining with specific reference to the single cache system. From the findings of the research, it was observed that the security in the cloud could be enhanced with the single cache system. For future purposes, an Apriori algorithm can be applied to the single cache system. This can be applied by all cloud providers, vendors, data distributors, and others. Further, data objects entered into the single cache system can be extended into 12 components. Database and SPSS modelers can be used to implement the same.
ProblemCloud computing is gaining popularity because of its features, including multitenancy, scalability, minimized maintenance, and hardware cost. Cloud technology gives the client multifold facilities, but it also brings additional privacy and
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