Contact tracing is a very powerful method to implement and enforce social distancing to avoid spreading of infectious diseases. The traditional approach of contact tracing is time consuming, manpower intensive, dangerous and prone to error due to fatigue or lack of skill. Due to this there is an emergence of mobile based applications for contact tracing. These applications primarily utilize a combination of GPS based absolute location and Bluetooth based relative location remitted from user's smartphone to infer various insights. These applications have eased the task of contact tracing; however, they also have severe implication on user's privacy, for example, mass surveillance, personal information leakage and additionally revealing the behavioral patterns of the user. This impact on user's privacy leads to trust deficit in these applications, and hence defeats their purpose.
In this work we discuss the various scenarios which a contact tracing application should be able to handle. We highlight the privacy handling of some of the prominent contact tracing applications. Additionally, we describe the various threat actors who can disrupt its working, or misuse end user's data, or hamper its mass adoption. Finally, we present privacy guidelines for contact tracing applications from different stakeholder's perspective. To best of our knowledge, this is the first generic work which provides privacy guidelines for contact tracing applications.
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adopti…
Johns Hopkins University（约翰斯·霍普金斯大学）；Microsoft Research（微软研究院）；University of Zurich（苏黎世大学）
A growing number of contact tracing apps are being developed to complement manual contact tracing. A key question is whether users will be willing to adopt these contact tracing apps. In this work, we survey over 4,500 Americans to evaluate (1) the effect of both accuracy and privacy concerns on reported willingness to install COVID19 contact tracing apps and (2) how different groups of users weight accuracy vs. privacy. Drawing on our findings from these first two research questions, we (3) quantitatively model how the amount of public health benefit (reduction in infection rate), amount of i…
Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada（格拉纳达大学安达卢西亚数据科学与计算智能研究所）
The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in their data silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data priva…
Morfix；OpenMined；École Supérieure en Informatique, Sidi Bel Abbès（西迪贝勒阿巴斯高等计算机学院）；Bitdefender（比特梵德）；Edinburgh Napier University（爱丁堡龙比亚大学）；apheris AI；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Tessella
We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting. Currently, our library supports C++, C, Go, WebAssembly, JavaScript, Python, and Rust, and runs on both traditional hardware (x86) and browser targets. We further apply our library to two use cases: (i) a privacy-preserving contact tracing protocol that is compatible with existing approaches,…
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), A-STAR（新加坡科技研究局资讯通信研究院）
Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance. Current knowledge tracing models are built based on an extensive set of data that are collected from multiple schools. However, it is impossible to pool learner's data from all schools, due to data privacy and PDPA policies. Hence, this paper explores the feasibility of building knowledge tracing models while preserving the privacy of learners' data within their respective schools. This study is conducted using part of the ASSISTment 2009 dataset, with data from multipl…
Concordia University（康考迪亚大学）；University of Calgary（卡尔加里大学）
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled…
Hong Kong University of Science and Technology（香港科技大学）；University of Guelph（圭尔夫大学）；University of Toronto（多伦多大学）
Businesses planning for the post-pandemic world are looking for innovative ways to protect the health and welfare of their employees and customers. Wireless technologies can play a key role in assisting contact tracing to quickly halt a local infection outbreak and prevent further spread. In this work, we present a wearable proximity and exposure notification solution based on a smartwatch that also promotes safe physical distancing in business, hospitality, or recreational facilities. Our proximity-based privacy-preserving contact tracing (P$^3$CT) leverages the Bluetooth Low Energy (BLE) tec…
Laboratoire de Physique de l’Ecole Normale Supérieure（巴黎高等师范学院物理实验室）；Université PSL（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；Université Paris-Diderot（巴黎第七大学）；Sorbonne Paris Cité（索邦巴黎西岱大学）；Politecnico di Torino（都灵理工大学）；Italian Institute for Genomic Medicine（意大利基因组医学研究所）；Collegio Carlo Alberto（卡洛·阿尔贝托学院）；INFN Sezione di Torino（意大利国家核物理研究所都灵分部）；The Abdus Salam International Centre for Theoretical Physics（阿卜杜斯·萨拉姆国际理论物理中心）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the …
Mila（米拉研究所）；University of Oxford（牛津大学）；Max-Planck Institute for Intelligent Systems Tübingen（马克斯·普朗克智能系统研究所蒂宾根分所）；University of Ottawa（渥太华大学）；McGill University（麦吉尔大学）；Université de Montreal（蒙特利尔大学）；École Polytechnique de Montreal（蒙特利尔理工学院）；The Alan Turing Institute（阿兰·图灵研究所）；HEC Montréal（蒙特利尔高等商学院）
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with correspo…
Indian Statistical Institute（印度统计研究所）；IBM Research（IBM研究院）
Amidst an increasing number of infected cases during the Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. To address this problem from an information retrieval (search) p…
