Decentralized Privacy-Preserving Proximity Tracing

Troncoso, Carmela; Payer, Mathias; Hubaux, Jean-Pierre; Salathé, Marcel; Larus, James R.; Bugnion, Edouard; Lueks, Wouter; Stadler, Theresa; Pyrgelis, Apostolos; Antonioli, Daniele; Barman, Ludovic; Chatel, Sylvain; Paterson, Kenneth G.; Čapkun, Srđjan; Basin, David; Beutel, Jan; Jackson, Dowdy; Roeschlin, Marc; Leu, P.; Preneel, Bart; Smart, Nigel P.; Abidin, Aysajan; Gürses, Seda; Veale, Michael; Cremers, Cas; Backes, Michael; Tippenhauer, Nils Ole; Binns, Reuben; Cattuto, Ciro; Barrat, Alain; Fiore, Dario; Barbosa, Manuel; Oliveira, Rui de; Pereira, José Alberto

doi:10.48550/arxiv.2005.12273

Cited by 35 publications

(69 citation statements)

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“…The runtimes of the LS/LS+ algorithms are also much lower than the baselines and do not depend on the network size since they are local algorithms -as opposed to the baselines, which have quadratic or even larger dependence on the network size. The "low-tech" approach in the design of the LS/LS+ algorithms increases their potential to be implemented in real-world scenarios, possibly even in a decentralized way, similarly to contact tracing smart phone applications [41], which is an interesting direction for future work. ), the middle household contains no symptomatic node (only the asymptomatic node 𝑣 3 ), but the LS+ algorithm still succeeds.…”

Section: Discussionmentioning

confidence: 99%

“…During the COVID-19 pandemic, we have seen a revolution of the contact tracing technology, which helped track and contain the epidemic [6,19]. Some contact tracing programs were conducted by governmental/health agencies [30], while others relied on decentralized approaches [41]. Most contact tracing approaches work by notifying people who could have received the infection from known infectious patients, i.e., they trace "forward" in time.…”

Section: Introductionmentioning

confidence: 99%

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Source Detection via Contact Tracing in the Presence of Asymptomatic Patients

Ódor¹,

Vuckovic²,

Ndoye³

et al. 2021

Preprint

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Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source detection algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Detection via Contact Tracing Framework (SDCTF). In the SDCTF, the source detection task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We propose two local search algorithms for the SDCTF: the LS algorithm is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform state of the art adaptive and non-adaptive source detection algorithms adapted to the SDCTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the probability of success of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source detection algorithms are tested on such a complex dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Source Detection via Contact Tracing in the Presence of Asymptomatic Patients

Ódor¹,

Vuckovic²,

Ndoye³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…As with all security solutions, the privacy guarantees of GAEN are relative. There certainly exist extreme scenarios (e.g., [3], [6], [7], [8], [19], [20]) where attackers may learn additional information. If an adversary has access to RPIs, TEKs, and RPI date-time information read by (say) thousands of users, then the adversary can profile a user's movement [3], [7], [6].…”

Section: Gaen's Privacy Wrt Threat Modelsmentioning

confidence: 99%

“…Hence, user privacy is preserved in the Some attack scenarios in Table 1 have rather strong assumptions regarding the complexity of the attack setup and demand huge resources. For example, attackers in the Organized Crime I model (ID 5) require TEKs and aggregated data in each 10 to 20-minute time window to deanonymize infected users [3], [6], [7]. Aggregated data include public and sensitive information, such as date, time, interaction graph, social graph, address, location type (e.g., residential, workplace, and library), and surveillance cameras.…”

Section: Gaen's Privacy Wrt Threat Modelsmentioning

confidence: 99%

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Privacy Guarantees of BLE Contact Tracing: A Case Study on COVIDWISE

Ahmed,

Xiao,

Taejoong

et al. 2021

Preprint

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Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification," to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust.

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A Data-Driven Simulation of the Exposure Notification Cascade for Digital Contact Tracing of SARS-CoV-2 in Zurich, Switzerland

et al. 2021

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IMPORTANCE Digital contact tracing (DCT) apps have been released in several countries to help interrupt SARS-CoV-2 transmission chains. However, the effect of DCT on pandemic mitigation remains to be demonstrated. OBJECTIVE To estimate key populations and performance indicators along the exposure notification cascade of the SwissCovid DCT app in a clearly defined regional and temporal context. DESIGN, SETTING, AND PARTICIPANTSThis comparative effectiveness study was based on a simulation informed by measured data from issued quarantine recommendations and positive SARS-CoV-2 test results after DCT exposure notifications in the canton of Zurich. A stochastic model was developed to re-create the DCT notification cascade for Zurich. Population sizes at each cascade step were estimated using triangulation based on publicly available administrative and observational research data for the study duration from September 1 to October 31, 2020. The resultant estimates were checked for internal consistency and consistency with upstream or downstream estimates in the cascade. Stochastic sampling from data-informed parameter distributions was performed to explore the robustness of results. Subsequently, key performance indicators were evaluated to assess the potential contribution of DCT compared with manual contact tracing. MAIN OUTCOMES AND MEASURES Receiving a voluntary quarantine recommendation and/or a positive SARS-CoV-2 test result after exposure notification. RESULTS In September 2020, 537 app users received a positive SARS-CoV-2 test result in Zurich, 324 of whom received and entered an upload authorization code. This code triggered an app notification for an estimated 1374 (95% simulation interval [SI], 932-2586) proximity contacts and led to 722 information hotline calls, with an estimated 170 callers (95% SI, 154-186) receiving a quarantine recommendation. An estimated 939 (95% SI, 720-1127) notified app users underwent testing for SARS-CoV-2, of whom 30 (95% SI, 23-36) had positive results after an app notification.Key indicator evaluations revealed that the DCT app triggered quarantine recommendations for the equivalent of 5% of all exposed contacts placed in quarantine by manual contact tracing. For every 10.9 (95% SI, 7.6-15.6) upload authorization codes entered in the app, 1 contact had positive test results for SARS-CoV-2 after app notification. Longitudinal indicator analyses demonstrated bottlenecks in the notification cascade, because capacity limits were reached owing to an increased incidence of SARS-CoV-2 infection in October 2020. CONCLUSIONS AND RELEVANCEIn this simulation study of the notification cascade of the SwissCovid DCT app, receipt of exposure notifications was associated with quarantine recommendations and identification of SARS-CoV-2-positive cases. These findings in notified proximity contacts reflect important intermediary steps toward transmission prevention.

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Decentralized Privacy-Preserving Proximity Tracing

Cited by 35 publications

References 0 publications

Source Detection via Contact Tracing in the Presence of Asymptomatic Patients

Source Detection via Contact Tracing in the Presence of Asymptomatic Patients

Privacy Guarantees of BLE Contact Tracing: A Case Study on COVIDWISE

A Data-Driven Simulation of the Exposure Notification Cascade for Digital Contact Tracing of SARS-CoV-2 in Zurich, Switzerland

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