Deploying decentralized, privacy-preserving proximity tracing

Troncoso, Carmela; Bogdanov, Dan; Bugnion, Edouard; Chatel, Sylvain; Cremers, Cas; Gürses, Seda; Hubaux, Jean-Pierre; Jackson, Dowdy; Larus, James R.; Lueks, Wouter; Oliveira, Rui; Payer, Mathias; Preneel, Bart; Pyrgelis, Apostolos; Salathé, Marcel; Stadler, Theresa; Veale, Michael

doi:10.1145/3524107

Cited by 53 publications

(58 citation statements)

References 3 publications

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“…Google/Apple Exposure Notification (GAEN) solution and Decentralised Privacy‐Preserving Proximity Tracing (DP3T) [1] are built based on this idea of sharing tokens via Bluetooth devices. As discussed in Section 1, the typical BLE‐based CT approach remains susceptible to various attacks.…”

Section: Related Workmentioning

confidence: 99%

“…The GPS enhancement is used for the implementation. The implementation builds on the legacy DP3T [1] Android app and the server whose code is available on Github. The location module is inspired with ideas from the Safe Paths approach [6], whose code is also available on Github.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…The SecureCT system performance is compared with other works, including the Google Apple approach, DP3T [1], PACT [27], Epione [14], Catalic [9], and PSI‐WCA protocol in [8] with respect to security and privacy guarantees, infrastructure requirements and client side cost in terms of computation and communication. The comparison is presented in Table 1.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…A large number of CT mobile applications (apps) have been developed and deployed, with most of them based on the exchange of random and anonymous tokens using Bluetooth (BT) [1–2]. These are generally decentralised systems that alert users if they may have come in close proximity with other positively diagnosed users (infected users).…”

Section: Introductionmentioning

confidence: 99%

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Secure contact tracing platform from simplest private set intersection cardinality

Gao

Surana

Trieu

2022

IET Information Security

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Contact tracing is an essential tool for controlling the spread of disease through human populations. However, existing contact tracing applications are either vulnerable to privacy and security attacks or heavy bandwidth/computational requirements on the client's devices. In this work, we introduce SecureCT, a Secure Contact Tracing platform with strong privacy protection and lightweight cost. SecureCT prevents linkage attacks, eliminates replay and relay attacks, and allows the phone's holder to delegate their contact tracing computation to untrusted servers while maintaining the user's privacy. The technical core of our scheme is an efficient Private Set Intersection Cardinality protocol which only relies on symmetric-key primitives. We evaluate its performance to show the feasibility of our proposed system in practice.

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Section: Related Workmentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure contact tracing platform from simplest private set intersection cardinality

Gao

Surana

Trieu

2022

IET Information Security

View full text Add to dashboard Cite

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“…The idea of integrating smartphones into contact tracing heightened privacy concerns in various ways in comparison with traditional person-to-person contact tracing methods [9,10]. States and various private entities around the world raced to devise contact tracing solutions that would incorporate privacy-by-design and privacy regulation into smartphone-based contact tracing systems [11]. Ultimately, like other attempts at mobile contact tracing before them [12], these attempts at privacysensitive smartphone-based contact tracing applications became a small part of a larger failure to use contact tracing to curb COVID-19 in the USA [13,14].…”

Section: Introductionmentioning

confidence: 99%

Privacy and contact tracing efficacy

Benthall

Hatna

Epstein

et al. 2022

J. R. Soc. Interface.

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As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual’s ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.

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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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Deploying decentralized, privacy-preserving proximity tracing

Abstract: Lessons from a pandemic.

Cited by 53 publications

References 3 publications

Secure contact tracing platform from simplest private set intersection cardinality

Secure contact tracing platform from simplest private set intersection cardinality

Privacy and contact tracing efficacy

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

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