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BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond

Hao Xu, Lei Zhang, Oluwakayode Onireti, Yang Fang, William Bill Buchanan, Muhammad Ali Imran

arXiv:2005.10103v3cs.DCcs.CR

TL;DR

Contact tracing requires sensitive information to be collected, matched, and shared, making privacy a central concern. BeepTrace uses blockchain to separate user identity from geodata and coordinate tracing between users and authorized solvers. The paper reports advantages in security, privacy, battery use, and coverage, while identifying traffic, computing, and throughput demands as deployment challenges.

  • Problem

    Because contact tracing requires information to be collected, matched, and distributed, protecting users’ identity and privacy is a central challenge.

    Source: D. Motivation · p. 4

  • Method

    BeepTrace uses blockchain to separate user identity from geodata and support tracing between users or patients and authorized solvers.

    Source: B. Workflow of the BeepTrace · p. 7

  • Results

    The authors report that BeepTrace is an all-around winner over existing solutions in security, privacy, battery, and coverage perspectives.

    Source: §VII · p. 13

  • Takeaways & Limitations

    The authors present BeepTrace as a framework for authorities, companies, developers, and researchers to develop and deploy contact-tracing applications.

    Source: §VII · p. 13

    The authors identify address traffic, geodata-matching computation, and single-chain throughput as major deployment challenges.

    Source: A. Network throughput and scalability · p. 12

Abstract

from arXiv · show

The outbreak of COVID-19 pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this paper, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contract tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers and researchers to fast develop and deploy effective digital contact tracing applications, to conquer COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

DISCLAIMER AND NOTICE OF THE LATEST VERSION

The paper situates BeepTrace amid the COVID-19 response and the shift toward smartphone contact tracing. It notes that traditional tracing is limited in completeness, timeliness, and efficiency, while digital approaches use different models for detecting and matching contacts.

DISCLAIMER AND NOTICE OF THE LATEST VERSION

  • The latest version of BeepTrace is available through IEEE Xplore; the paper notes that its arXiv version may no longer be accessible after copyright transfer.The notice provides the IEEE Xplore document link.Source: DISCLAIMER AND NOTICE OF THE LATEST… · p. 1

I. INTRODUCTION

  • Contact tracing is presented as a pillar of COVID-19 control and as a way to support easing social-distancing restrictions while saving lives and restoring normality.The paper describes the economic costs of strict measures and governments’ interest in balancing them against the risk of renewed infections.Source: §I · p. 2

A. Contact tracing

  • Traditional contact tracing relies on recalled contact lists and follow-up messages, limiting the completeness, accuracy, timeliness, and efficiency of tracing.Smartphone approaches include Bluetooth encounter detection, with centralized server matching or decentralized matching on users’ devices.Source: A. Contact tracing · pp. 2–3

B. Review of existing contact tracing solutions

The paper reviews privacy and operational concerns in existing tracing approaches, then motivates blockchain as a way to separate identity from location data. Its design goals include privacy, broad tracing reach, verifiable information, and user control over data sharing.

B. Review of existing contact tracing solutions

  • Bluetooth contact-tracing approaches raise concerns about battery use, wireless vulnerabilities, and privacy risks associated with centralized matching or access to server information.The review discusses TraceTogether and Google/Apple-style approaches, as well as similar privacy risks for the NHS app.Source: B. Review of existing contact tracing… · p. 3

C. Blockchain basis for contact tracing

  • Blockchain is proposed as a distributed bridge between users or patients and authorized solvers, separating user identity from location information during tracing.The paper also attributes global access and transparent verification of tracing information to blockchain.Source: C. Blockchain basis for contact tracing · p. 4

D. Motivation

  • The paper treats privacy as a primary design requirement, arguing that more collected information may improve tracing but privacy should not be sacrificed.It also identifies secure data sharing and risks from centralized control as concerns.Source: D. Motivation · p. 4
  • The authors seek a globally accessible tracing network that can incorporate different positioning technologies and serve users across geographic or political boundaries.They contrast this goal with decentralized solutions limited to local networks and with reach constrained by political or technical factors.Source: D. Motivation · p. 4
  • The paper presents blockchain transparency as a way for the public to verify tracing information, while trusted authorities confirm results, geographic matches, and notifications.This is offered as a response to concerns about inaccurate information and misinformation.Source: D. Motivation · p. 4
  • The proposed credential-management approach is intended to let users share or revoke sharing and to manage sensitive data through its generation, sharing, and disposal.The paper says agencies should limit sharing within trusted partnerships and storage should follow applicable recommendations.Source: D. Motivation · p. 4

E. Contributions

BeepTrace proposes a blockchain-enabled contact-tracing framework that separates user identity from geodata and supports sharing among users, solvers, and other providers. The paper also discusses blockchain consensus choices and their performance trade-offs for this setting.

E. Contributions

  • BeepTrace is proposed to address limitations of labor-intensive and Bluetooth-restrained tracing while aiming to protect privacy across data generation, sharing, and disposal.The paper describes its goal as making tracing information sharing more efficient without sacrificing privacy.Source: E. Contributions · p. 4
  • The two-chain design gives authorized solvers access to desensitized location information for matching, while exposed users receive pseudonyms or fingerprints for local self-matching.The paper also reports numerical analyses of network storage, computing capacity, and individual user data consumption.Source: E. Contributions · p. 5
  • The authors present BeepTrace as an open framework for worldwide collaboration among authorities, companies, developers, and researchers.The stated aim is to develop and deploy tracing-information sharing without invading privacy rights.Source: E. Contributions · p. 5

II. BLOCKCHAIN AS THE BACKBONE FOR PRIVACY-PRESERVING INFORMATION SHARING

  • The paper identifies throughput, delay, storage, and scalability as considerations when selecting a blockchain consensus mechanism for BeepTrace.It describes Proof of Work and Proof of Stake as resource-intensive or throughput-limited options for large populations, with long confirmation delays.Source: §II · p. 6
  • The authors describe DAG-based consensus as allowing concurrent transaction branches, low user resource use, zero transaction fees, and high confirmation rates and throughput.The paper says users can insert transactions after processing earlier transactions.Source: §II · p. 6

III. BLOCKCHAIN ENABLED PRIVACY-PRESERVING CONTACT TRACING

The framework combines user devices, diagnosticians, geodata solvers, key authorities, positioning providers, and two blockchains. It is presented as an open interface that can accommodate different contact-tracing methods, consensus mechanisms, incentives, and positioning services.

III. BLOCKCHAIN ENABLED PRIVACY-PRESERVING CONTACT TRACING

  • BeepTrace is described as an open interface for sharing contact-tracing information across different methods, authorities, and cryptography.The framework does not restrict positioning services, consensus mechanisms, or blockchain incentive mechanisms, provided consensus fits network performance requirements.Source: §III · p. 6

A. Entities, functions and interfaces

  • Users include confirmed patients and healthy app users, who upload encrypted TraceCodes to the tracing blockchain and read the notification blockchain for self-matching.The paper uses “user” to refer to the user equipment, app, and device.Source: A. Entities, functions and interfaces · p. 6
  • Diagnosticians endorse confirmed users’ geodata, while geodata solvers read tracing-chain data for matching and send matched data to the notification blockchain.Solvers may be servers or server clusters associated with a trusted third party or a user.Source: A. Entities, functions and interfaces · p. 6
  • TraceCode is a blockchain address composed of a user pseudonym prefix and an encrypted-geodata suffix; the tracing blockchain supports registration and matching, while the notification blockchain registers risk.A certified authority distributes keys, and positioning providers may supply GPS, Bluetooth, cellular-tower, or Wi-Fi data.Source: A. Entities, functions and interfaces · pp. 6–7

B. Workflow of the BeepTrace

BeepTrace links pseudonymous user addresses with encrypted, timestamped geodata, then changes the linkage after diagnosis so authorized solvers can trace potential exposure without learning users’ identities. Users receive risk notifications through a separate blockchain and check them locally.

Workflow of the BeepTrace

  • Users generate local private keys over time for pseudonyms, then encrypt timestamped location data with a CA-certified public key to form each TraceCode’s geodata suffix.Users declare the address on the blockchain, where a trusted third party can index it by suffix while the pseudonym keeps identity anonymous.Source: B. Workflow of the BeepTrace · p. 7
  • After diagnosis and patient consent, the diagnostician receives and verifies the patient’s pseudonyms, replaces their prefix with a diagnostician-associated encrypted value, and endorses the recoupled address.The replacement prefix may use man-made drifting or noise encryption.Source: B. Workflow of the BeepTrace · p. 7
  • Anyone with access to the tracing blockchain can read the diagnosis update, but only holders of the geo private key can decrypt geodata, and the pseudonym link remains known only to the user.The passage states that diagnosis-related privacy is revealed during diagnosis and is protected under regulations and laws.Source: B. Workflow of the BeepTrace · p. 7
  • Authorized solvers match diagnosed patients’ decrypted geodata and timestamps against addresses, endorse risk updates, and publish them to a separate notification blockchain for local user matching.Users can match compressed notification results using their prefix fingerprints; the process is described as preserving privacy locally because others cannot link users to geodata without their keys.Source: B. Workflow of the BeepTrace · p. 7
  • An optional symptoms-code sub-address scheme can support self-marking without CA involvement, but unsigned information is untrusted unless the community has a broad trust basis.Source: B. Workflow of the BeepTrace · p. 7

C. Blockchain pseudo-identity sharing

BeepTrace’s TraceCode combines a user pseudonym and encrypted geodata in one blockchain address. The design limits what public readers and blockchain participants can learn while allowing authorized parties to decrypt geodata.

Blockchain pseudo-identity sharing

  • A TraceCode address joins a user-generated pseudonym prefix to a CA-public-key-encrypted geodata suffix, creating a direct link between pseudo-identity and geodata.The diagnostician verifies the user’s private key to confirm rightful pseudonym ownership before sharing diagnosis information.Source: C. Blockchain pseudo-identity sharing · p. 8
  • Authorized users or servers can decrypt geodata with the authority’s private key but cannot identify the pseudonym, while the two blockchain chains publish only selective information from trusted sources.Source: C. Blockchain pseudo-identity sharing · p. 8
  • User connectivity traces such as IP addresses, routing information, and ISP records are not inherited on the blockchain, even if miners receive them.The passage says those records are isolated from the blockchain network.Source: C. Blockchain pseudo-identity sharing · p. 9

IV. GEODATA GENERALIZATION FOR PRIVACY PROTECTION AND SOLVING

BeepTrace accommodates diverse geodata sources, but geodata overhearing and the balance between privacy and accuracy remain challenges. Its proposed generalization approach perturbs or coarsens location data before sharing and matching.

Geodata generalization for privacy protection and solving

  • Contact tracing can use GPS, Wi-Fi, Bluetooth, base-station, and other indoor or outdoor positioning data, but geodata overhearing and privacy-versus-accuracy granularity remain challenges.Source: §IV · p. 9
  • GPS/Wi-Fi/cellular services generate raw geodata, which users upload to the blockchain network with a CA-issued public key; meanwhile, it is perturbed to prevent identical-suffix-match tracing against the patient’s private key.Perturbations include adding salt or transforming the geodata. Examples include GCJ-02 datum and converting WGS84 to the accuracy-limited OSNG: OSGB36 grid reference system.Source: §IV · p. 9
  • Users choose the geodata detail level subject to local-agency accuracy requirements, and diagnosticians can further coarsen data or reconstruct it with a dedicated key for secured sharing.The passage links geodata management to fine-grained blockchain access control.Source: §IV · p. 9

A. Geodata solving and reverse topological cross-infection warning

The solving process compares patient geodata with collected records to assess contact risk. It can also incorporate Bluetooth grouping and third-party GIS information to broaden location-based warnings.

Geodata solving and reverse topological cross-infection warning

  • Solvers decrypt geodata and compare each patient record with collected data from the previous 14 days for COVID-19, using distance and contact duration to assess risk.The passage says the 14-day period is WHO-recommended for COVID-19 and may differ for other pandemics.Source: A. Geodata solving and reverse… · p. 9
  • Bluetooth group information can be integrated when users agree to link it, supplementing proximity-based tracing with other positioning services.Source: A. Geodata solving and reverse… · p. 9
  • Solvers can link locations with OpenStreetMap data such as road topology or building function, then use that context to warn people who were at the same location and time.The passage gives a shopping mall as an example of using topology to identify others to warn.Source: A. Geodata solving and reverse… · p. 9

B. Risk level management and notification

BeepTrace combines passive risk notifications with user-controlled key revocation and a tracing design intended to preserve privacy while supporting battery-friendly, broader-coverage contact tracing.

B. Risk level management and notification

  • Government-guided distance and contact-time criteria determine risk endorsements, which solvers link to addresses for passive notification.For example, users within 10 meters for more than 15 minutes are marked high-risk; users actively look up a prefix to identify themselves.Source: B. Risk level management and… · pp. 9–10

C. Complete freedom of pseudonym revoking and sifting

  • Users can change private keys to reduce trajectory-tracing risk or ask the PKI/CA to revoke a public key, at the cost of increased data consumption and storage.The paper says key changes can prevent retrospective use of logs to reveal activity patterns.Source: C. Complete freedom of pseudonym… · p. 10

V. RESULTS

A. Comparisons with existing contact tracing solutions

  • BeepTrace records information in the background and transmits at suitable times, while its decentralized service uses passive listening to protect privacy during matching.The paper also describes the service as power-preserving and battery-friendly, with location privacy managed through the data life cycle.Source: A. Comparisons with existing contact… · p. 10
  • Integrated user and third-party services are intended to extend BeepTrace coverage globally, unlike Bluetooth solutions whose coverage is limited to local operation.Source: A. Comparisons with existing contact… · p. 10

B. Blockchain performance requirement

The resource analysis finds that geodata matching becomes impractical at billion-user scale, while blockchain storage and transaction throughput pose distinct deployment demands.

B. Blockchain performance requirement

C. Geodata computing resource requirement

  • Under an assumed lookup time below 0.1 ms per record, matching grows linearly with daily confirmed cases but exponentially with total participants.Source: C. Geodata computing resource… · p. 11
  • A 70-million-user geodata network is manageable with a handful of high-performance cluster servers, whereas a 4-billion-user network is impractical with current technology.Source: C. Geodata computing resource… · p. 11

D. User-side resources requirements

Geodata matching demand rises sharply with network size, while user-side storage and data consumption require compression. The paper identifies regional scaling as a response to network and throughput constraints.

D. User-side resources requirements

  • Users act as thin blockchain nodes, retrieving data and performing lookups locally, while the authors assume 15 addresses per location associated with a patient.This local processing uses user computing, storage, and network resources; the passage says it preserves user privacy.Source: D. User-side resources requirements · p. 11
  • Compression reduces daily data consumption to 33.6 MB at R = 15 or 6.7 MB at R = 3, with 210 records of 16-byte fingerprints over 14 days.The solver summarizes daily records and changes pseudonyms daily; uploading 28 addresses of 64 bytes is less than downloading notification data.Source: D. User-side resources requirements · p. 11

A. Network throughput and scalability

  • The authors identify massive address-declaration traffic and matching compute demand as challenges, and propose regional chains to divide users into smaller groups.They note that hundreds of millions of transactions per second would challenge any existing blockchain, while DAGs are described as theoretically having no throughput limit.Source: A. Network throughput and scalability · p. 12
  • A 70-million-person network needs dozens of AWS EC2 p3dn.24xlarge instances, while matching 4 billion people would require resources equivalent to 23 Summit supercomputers.The authors describe the larger-network requirement as hardly achievable with current technology.Source: A. Network throughput and scalability · p. 12

B. Battery drainage and storage optimization

BeepTrace separates geodata recording from uploading to reduce battery demands, and discusses provisions for vulnerable users and the system’s economic and social conditions. The conclusion characterizes its numerical results as favorable across security, privacy, battery, and coverage.

B. Battery drainage and storage optimization

  • Users can store geodata locally and upload it while charging over WiFi, accepting notification delays of a few hours because tracing already involves lag.Geodata generation pauses when locations do not change, and solvers upload only unique addresses to reduce entries.Source: B. Battery drainage and storage… · p. 12

C. Technology for elders and minors

  • Wearables and wireless IoT can include elders and minors, with private keys transferable to guardians through a secured device-to-device channel.Trusted third parties may send push notifications to vulnerable users with their privacy consent.Source: C. Technology for elders and minors · p. 12

D. Economical and social aspects

  • The paper notes that decentralized blockchain may cost taxpayers more than centralized systems, while its non-governmental character may reduce privacy concerns and resistance.The authors connect this acceptance to increased uptake of digital contact tracing.Source: D. Economical and social aspects · pp. 12–13
  • BeepTrace may struggle to attract independent miners without transaction fees; proposed rewards include government payment or voluntary user participation.The authors also suggest that governments could reimburse fees when existing blockchains are shared.Source: D. Economical and social aspects · p. 13

VII. CONCLUSIONS

  • The authors conclude that BeepTrace’s numerical results make it an all-around winner for security, privacy, battery use, and coverage.They present it as a framework for authorities, companies, and research institutes to develop tracing information-sharing platforms.Source: §VII · p. 13
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