Abstract
The recent pandemic of novel coronavirus introduces a new challenge to balancing social welfare, the economy, and privacy while reducing contact among individuals. To reduce the reproduction rate without spoiling our economy, we need good incentive mechanisms to reduce the possibility of spreading the virus as well as good privacy enhancing techniques. Unfortunately, some of the recent approaches in contact tracing are not successful due to privacy concerns and a lack of sufficient incentive mechanisms to guide behavior instead of simply tracking infections. In this paper, we provide a design using smart contracts as an incentive mechanism with enhanced privacy of user location information. We utilize encrypted data calculated from a set of network routing information, and a plaintext equality test of a public key cryptosystem to estimate the duration one is present at the same location. By staying at the same location longer, a user can obtain greater rewards. We have implemented a proof concept of this scheme to evaluate its efficiency. We also discuss financial regulation and economic viewpoints.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Usage of TEE at user device is not for operation of blockchain nodes.
- 2.
One could very well stop after creating a histogram of the location distribution, but a user’s performance is less clear when viewed categorically and hence we prefer to have a derived metric that greatly simplifies the act of comparing user performance.
- 3.
The experiment was run on a mid-tier personal computer. This figure can likely be greatly reduced on higher end hardware.
References
TraceTogether. https://www.tracetogether.gov.sg/
Anciaux, N., et al.: Anonymous tracing, dangerous oxymoron (2020). https://risques-tracage.fr/docs/risques-tracage.pdf
Canard, S., Pointcheval, D., Santos, Q., Traoré, J.: Privacy-preserving plaintext-equality of low-entropy inputs. In: Preneel, B., Vercauteren, F. (eds.) ACNS 2018. LNCS, vol. 10892, pp. 262–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93387-0_14
Kaafar, D., et al.: Joint Statement on Contact Tracing (2020). https://t.co/y803WQ0nV6
The Financial Crimes Enforcement Network (FinCEN). Application of FinCEN’s regulations to certain business models involving convertible virtual currencies—fincen.gov, May 2019. https://www.fincen.gov/resources/statutes-regulations/guidance/application-fincens-regulations-certain-business-models
Centres for Disease Control and Prevention. Case investigation and contact tracing: part of a multipronged approach to fight the COVID-19 pandemic—CDC. https://www.cdc.gov/coronavirus/2019-ncov/php/principles-contact-tracing.html
Garay, J., Kiayias, A., Leonardos, N.: The bitcoin backbone protocol: analysis and applications. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9057, pp. 281–310. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46803-6_10
Hinch, R., et al.: Effective configurations of a digital contact tracing app: a report to NHSX. https://www.research.ox.ac.uk/Article/2020-04-16-digital-contact-tracing-can-slow-or-even-stop-coronavirus-transmission-and-ease-us-out-of-lockdown
Apple Inc. and Google Inc.: Privacy-preserving contact tracing - apple and Google. https://www.apple.com/covid19/contacttracing
Kelly, L.: Top Researchers Condemn Centralized COVID-19 Tracking Approach (2020). https://cryptobriefing.com/top-researchers-condemn-centralized-covid-19-tracking-approach/
Sabt, M., Achemlal, M., Bouabdallah, A.: Trusted execution environment: what it is, and what it is not. In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol. 1, pp. 57–64 (2015)
Su, J., Bartholic, M., Stange, A., Ushida, R., Matsuo, S.I.: How to dynamically incentivize sufficient level of IoT security. In: 4th Workshop on Trusted Smart Contracts, pp. 57–64 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bartholic, M., Su, J., Ushida, R., Ikeno, Y., Gu, Z., Matsuo, S. (2020). Proof of No-Work: How to Incentivize Individuals to Stay at Home. In: Garcia-Alfaro, J., Navarro-Arribas, G., Herrera-Joancomarti, J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2020 2020. Lecture Notes in Computer Science(), vol 12484. Springer, Cham. https://doi.org/10.1007/978-3-030-66172-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-66172-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66171-7
Online ISBN: 978-3-030-66172-4
eBook Packages: Computer ScienceComputer Science (R0)