ABSTRACT
In the post-epidemic era of normalized COVID-19, digital contact tracing will continue to be used as an efficient epidemiological investigation tool. Meanwhile, its widespread use has raised concerns about long-term data privacy and security. In this paper, an effective method for privacy concerns on digital contact tracing protocols (DCTP) is proposed. Digital contact tracing protocols are classified from both technical and architectural perspective. Then, the LINDDUN (each letter of "LINDDUN"stands for a privacy threat type) privacy threat modeling framework is used to analyze the privacy threats that may be contained in different protocols. Last, mitigation strategies are provided to advance privacy protection for future digital contact tracing protocols. The experiment show that the proposed method significantly outperforms existing methods. © 2022 IEEE.
ABSTRACT
As social media becomes more and more popular, fake news spreads rapidly which is more likely to cause serious consequences, especially during the COVID-19 pandemic. On the premise of meeting data privacy and security requirements, federated learning uses multi-party heterogeneous data to further promote machine learning. This paper proposes a federal learning based COVID-19 fake news detection model with deep self-attention network (FL-FNDM). We construct a deep self-attention network for fake news detection, which combines self-attention-based pretrained model BERT and deep convolutional neural network to detect fake news. Moreover, the fake news detection model is learned under the framework of horizontal federated learning, aiming at protecting users' data security and privacy. The experimental results demonstrate that the proposed model can improve the performance of fake news detection on the COVID-19 dataset, which can achieve almost the same effect of sharing data without leaking user data. © 2021 IEEE.
ABSTRACT
Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Given the high rates of contagion, which makes difficult an effective manual tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations. ©2021 IEEE