Privacy Preservation of User Identity in Contact Tracing for COVID-19-Like Pandemics Using Edge Computing
TL;DR: In this paper, the authors proposed a new system that enables health authorities to track exposure among individuals participating in the automated system, aid health authorities in interviewing non-participating individuals, and minimize the processing required by offloading to nearby edge computing devices.
Abstract: Pandemic and infectious disease outbreaks put pressure on health authorities and require lockdowns. These outbreaks, which strain limited healthcare resources, must be swiftly controlled and monitored. A large number of healthcare authorities are currently investigating automated systems to support outbreak monitoring and control. However, current contact tracing systems face many privacy, participation, and power constraints. Furthermore, elderly or less financially able individuals often cannot participate in automated contact tracing due to not owning a smartphone. This paper proposes a new system that enables health authorities to track exposure among individuals participating in the automated system, aid health authorities in interviewing non-participating individuals, and minimize the processing required by offloading to nearby edge computing devices. The proposed system utilizes edge servers to assist health authorities in tracking users who withdraw from or are unable to use contact tracing. Edge computing devices have access to more contextual information, resulting in minimal data collection and thus enabling businesses, houses, and offices to participate in contact tracing as locations. Edge computing devices enable location-based data collection of contact tracing data using proximity-based sensors for offices, homes, and shops, thereby assisting health authorities to notify users of exposure without disclosing the identities of businesses or individuals. Moreover, the proposed system reduces the overall power for end users up to 97% by delegating contact tracing to nearby edge computing devices. In addition, the system mitigates data poisoning attacks that target individuals’ smartphones, edge devices, or cloud servers by utilizing blockchain to store contacts, delegations, and identities.
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TL;DR: Wang et al. as discussed by the authors proposed a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation to preserve the full-fledged privacy protection property with a lower computational cost.
14 citations
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TL;DR: It is confirmed that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded, so existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
Abstract: Digital contact tracing is being used by many countries to help contain COVID-19's spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users' willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
2 citations
TL;DR: In this article , the authors compared lightweight neural architectures for COVID-19 identification using chest X-rays, highlighting the strengths and weaknesses of each approach, and concluded that additional research is necessary to improve the accuracy of lightweight models and make them practical for real-world use.
Abstract: The COVID-19 pandemic has had a global impact, transforming how we manage infectious diseases and interact socially. Researchers from various fields have worked tirelessly to develop vaccines on an unprecedented scale, while different countries have developed various sanitary protocols to deal with more contagious variants. Machine learning-assisted diagnosis has emerged as a powerful tool that can help health professionals deliver faster and more accurate outcomes. However, medical systems that rely on deep learning often require extensive data, which may be impractical for real-world applications. This paper compares lightweight neural architectures for COVID-19 identification using chest X-rays, highlighting the strengths and weaknesses of each approach. Additionally, a web tool has been developed that accepts chest computer tomography images and outputs the probability of COVID-19 infection along with a heatmap of the regions used by the intelligent system to make this determination. The experiments indicate that most lightweight architectures considered in the study can identify COVID-19 correctly, but further investigation is necessary. Lightweight neural architectures show promise in computer-aided COVID-19 diagnosis using chest X-rays, but they did not reach accuracy rates above 88%, which is necessary for medical applications. These findings suggest that additional research is necessary to improve the accuracy of lightweight models and make them practical for real-world use.
TL;DR: A comprehensive survey on the integration of Blockchain and IoT (BCIoT) for Healthcare services, focusing mainly on existing approaches, possibilities, applications and challenges is presented in this article .
Abstract: Healthcare is an emerging sector with the integration of emerging technologies aiming to improve Quality of Life of an individual through various medical services. Most of the healthcare services work with sensitive information of patients either collected in real-time using body implanted sensors or through various IoT enabled medical devices during the diagnosis in a centrally controlled model. But, the traditional IoT based medical services suffer from several challenges such as data security, privacy, interoperability, single point of failure, scalability, and data integrity. However, by considering the advantages of Blockchain technology and the disadvantages of IoT systems, the amalgamation of a decentralised, distributed ledger technology with the IoT for various healthcare applications will strengthen the system by resolving the major challenges. Thus, this research article conducts a comprehensive survey on the integration of Blockchain and IoT (BCIoT) for Healthcare services, focusing mainly on existing approaches, possibilities, applications and challenges. First, we present a detailed overview of Blockchain, IoT and the motivation for BCIoT along with the survey on existing healthcare applications. Next we discuss the enabling platforms for BCIoT based healthcare services. For the better understanding, we review the role of BCIoT in Remote patient monitoring, electronic heath record management, Health asset tracing, Covid-19 infected patient contact tracking. Finally challenges and future directions are discussed to improve the Quality of Life of patients through Healthcare applications.
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TL;DR: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key.
Abstract: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.
14,659 citations
TL;DR: Public health measures were decisive in controlling the SARS epidemic in 2003 but whether these measures will be sufficient to control 2019-nCoV depends on addressing some unanswered questions.
Abstract: Public health measures were decisive in controlling the SARS epidemic in 2003. Isolation is the separation of ill persons from non-infected persons. Quarantine is movement restriction, often with fever surveillance, of contacts when it is not evident whether they have been infected but are not yet symptomatic or have not been infected. Community containment includes measures that range from increasing social distancing to community-wide quarantine. Whether these measures will be sufficient to control 2019-nCoV depends on addressing some unanswered questions.
1,756 citations
TL;DR: A rapid review on the effectiveness of quarantine during severe coronavirus outbreaks found that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic.
Abstract: Background Coronavirus disease 2019 (COVID-19) is a rapidly emerging disease classified as a pandemic by the World Health Organization (WHO). To support the WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks. Objectives To assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed or suspected cases of COVID-19, who travelled from countries with a declared outbreak, or who live in regions with high disease transmission. Search methods An information specialist searched the Cochrane COVID-19 Study Register, and updated the search in PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 23 June 2020. Selection criteria Cohort studies, case-control studies, time series, interrupted time series, case series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID-19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak. Data collection and analysis Two review authors independently screened abstracts and titles in duplicate. Two review authors then independently screened all potentially relevant full-text publications. One review author extracted data, assessed the risk of bias and assessed the certainty of evidence with GRADE and a second review author checked the assessment. We used three different tools to assess risk of bias, depending on the study design: ROBINS-I for non-randomised studies of interventions, a tool provided by Cochrane Childhood Cancer for non-randomised, non-controlled studies, and recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) for modelling studies. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and costs. Main results We included 51 studies; 4 observational studies and 28 modelling studies on COVID-19, one observational and one modelling study on MERS, three observational and 11 modelling studies on SARS, and three modelling studies on SARS and other infectious diseases. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta-analysis and undertook a narrative synthesis. We judged risk of bias to be moderate for 2/3 non-randomized studies of interventions (NRSIs) and serious for 1/3 NRSI. We rated risk of bias moderate for 4/5 non-controlled cohort studies, and serious for 1/5. We rated modelling studies as having no concerns for 13 studies, moderate concerns for 17 studies and major concerns for 13 studies. Quarantine for individuals who were in contact with a confirmed/suspected COVID-19 case in comparison to no quarantine Modelling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases may have averted 44% to 96% of incident cases and 31% to 76% of deaths compared to no measures based on different scenarios (incident cases: 6 modelling studies on COVID-19, 1 on SARS; mortality: 2 modelling studies on COVID-19, 1 on SARS, low-certainty evidence). Studies also indicated that there may be a reduction in the basic reproduction number ranging from 37% to 88% due to the implementation of quarantine (5 modelling studies on COVID-19, low-certainty evidence). Very low-certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings may be (2 modelling studies on SARS). Quarantine in combination with other measures to contain COVID-19 in comparison to other measures without quarantine or no measures When the models combined quarantine with other prevention and control measures, such as school closures, travel restrictions and social distancing, the models demonstrated that there may be a larger effect on the reduction of new cases, transmissions and deaths than measures without quarantine or no interventions (incident cases: 9 modelling studies on COVID-19; onward transmission: 5 modelling studies on COVID-19; mortality: 5 modelling studies on COVID-19, low-certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID-19. Quarantine for individuals travelling from a country with a declared COVID-19 outbreak compared to no quarantine Very low-certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths may be small for SARS, but might be larger for COVID-19 (2 observational studies on COVID-19 and 2 observational studies on SARS). Authors' conclusions The current evidence is limited because most studies on COVID-19 are mathematical modelling studies that make different assumptions on important model parameters. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID-19 pandemic, although there is uncertainty over the magnitude of the effect. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak and the impact of the measures implemented. This review was originally commissioned by the WHO and supported by Danube-University-Krems. The update was self-initiated by the review authors.
715 citations
TL;DR: This article provides the first comprehensive review of tracing apps' key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability, and presents an overview of many proposed tracing app examples.
Abstract: The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability In this article, we provide the first comprehensive review of these much-discussed tracing app attributes We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large
510 citations
TL;DR: Emerging technologies used to mitigate the threats of COVID-19 and relevant challenges related to technology design, development, and use are examined and insights and suggestions into how information systems and technology scholars can help fight the CO VID-19 pandemic are provided.
229 citations