Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy

Berke, Alex; Vepakomma, Praneeth; Larson, Kent; Pentland, Alex

doi:10.48550/arxiv.2003.14412

Cited by 8 publications

(11 citation statements)

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“…For example, CoEpi [9], Covid Watch [10], and the Decentralized Privacy-Preserving Proximity Tracing [11] (DP-3T) are all proposing solutions to implement anonymous contact tracing using smartphones' Bluetooth. The application proposed in Reference [12], instead, is based on GPS location history, relying on transformation of data and encryption to preserve privacy.…”

Section: Related Workmentioning

confidence: 99%

“…In the early days of the outbreak, with few cases, contact tracing could be done manually, while the growth of cases made it more difficult [5]. For these reasons, several proposals to automate the contact tracing have been presented [6][7][8][9][10][11][12], using smartphones' Bluetooth connectivity or GPS location history to perform the tracing.…”

Section: Introductionmentioning

confidence: 99%

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An Internet of Things Approach to Contact Tracing—The BubbleBox System

et al. 2020

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The COVID-19 pandemic exploded at the beginning of 2020, with over four million cases in five months, overwhelming the healthcare sector. Several national governments decided to adopt containment measures, such as lockdowns, social distancing, and quarantine. Among these measures, contact tracing can contribute in bringing under control the outbreak, as quickly identifying contacts to isolate suspected cases can limit the number of infected people. In this paper we present BubbleBox, a system relying on a dedicated device to perform contact tracing. BubbleBox integrates Internet of Things and software technologies into different components to achieve its goal—providing a tool to quickly react to further outbreaks, by allowing health operators to rapidly reach and test possible infected people. This paper describes the BubbleBox architecture, presents its prototype implementation, and discusses its pros and cons, also dealing with privacy concerns.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Internet of Things Approach to Contact Tracing—The BubbleBox System

et al. 2020

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“…Finally, [15] proposes partitioning GPS and time data into discrete spatiotemporal points and obfuscating these points using a one-way hash function. Infected users upload their obfuscated location histories after redacting personally identifiable information such as the GPS coordinates that represent a home or work address.…”

Section: Performance Evaluationmentioning

confidence: 99%

SNARKs to the rescue: proof-of-contact in zero knowledge

Ratliff,

Khoury

2020

Preprint

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This paper describes techniques to help with COVID-19 automated contact tracing, and with the restoration efforts. We describe a decentralized protocol for "proof-of-contact" (PoC) in zero knowledge where a person can publish a short cryptographic proof attesting to the fact that they have been infected and that they have come in contact with a set of people without revealing any information about any of the people involved. More importantly, we describe how to compose these proofs to support broader functionality such as proofs of nth-order (transitive) exposure which can further speed up automated contact tracing. The cryptographic proofs can be publicly verified, and in both cases, the burden is on the person proving contact and not on third parties or healthcare providers rendering the system more decentralized, and accordingly more scalable.

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“…In most cases, users can be monitored and tracked in real-time without users consent. If such mobile applications store the location history, user movement can be traced as well [14]. Apart from location tracking, there are several other user privacy issues, such as excessive data collection, obscure data flow, lack of user control, and data usage policies.…”

Section: B Contact Tracing Framework and Applicationsmentioning

confidence: 99%