A Note on Cryptographic Algorithms for Private Data Analysis in Contact Tracing Applications

Contact tracing is an important measure to counter the COVID-19 pandemic. In the early phase, many countries employed manual contact tracing to contain the rate of disease spread, however it has many issues. The manual approach is cumbersome, time consuming and also requires active participation of a large number of people to realize it. In order to overcome these drawbacks, digital contact tracing has been proposed that typically involves deploying a contact tracing application on people's mobile devices which can track their movements and close social interactions. While studies suggest that digital contact tracing is more effective than manual contact tracing, it has been observed that higher adoption rates of the contact tracing app may result in a better controlled epidemic. This also increases the confidence in the accuracy of the collected data and the subsequent analytics. One key reason for low adoption rate of contact tracing applications is the concern about individual privacy. In fact, several studies report that contact tracing applications deployed in multiple countries are not privacy friendly and have potential to be used for mass surveillance by the concerned governments. Hence, privacy respecting contact tracing application is the need of the hour that can lead to highly effective, efficient contact tracing. As part of this study, we focus on various cryptographic techniques that can help in addressing the Private Set Intersection problem which lies at the heart of privacy respecting contact tracing. We analyze the computation and communication complexities of these techniques under the typical client-server architecture utilized by contact tracing applications. Further we evaluate those computation and communication complexity expressions for India scenario and thus identify cryptographic techniques that can be more suitably deployed there.

• Publication year

  2020

• Venue

  arXiv.org

• Publication date

  2020-05-19

• Fields of study

  Computer Science

• Identifiers
  arXiv 2005.10634
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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

  2020cited by this paper
• Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App

  2020cited by this paper
• Privacy Guidelines for Contact Tracing Applications

  2020cited by this paper
• Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

  2020cited by this paper
• Decentralized Privacy-Preserving Proximity Tracing

  2020cited by this paper
• Private Set Intersection with Linear Communication from General Assumptions

  2019cited by this paper
• Private Categorization

  2019cited by this paper
• Scalable Private Set Intersection Based on OT Extension

  2018cited by this paper
• Private Set Intersection for Unequal Set Sizes with Mobile Applications

  2017cited by this paper
• Phasing: Private Set Intersection Using Permutation-based Hashing

  2015cited by this paper
• Private Set Intersection: Are Garbled Circuits Better than Custom Protocols?

  2012cited by this paper
• Practical Private Set Intersection Protocols with Linear Complexity

  2010cited by this paper
• An improved data stream summary: the count-min sketch and its applications

  2004cited by this paper
• Efficient Private Matching and Set Intersection

  2004cited by this paper
• Public-Key Cryptosystems Based on Composite Degree Residuosity Classes

  1999cited by this paper
• Analysis and design of cryptographic hash functions

  1999cited by this paper
• A More Efficient Cryptographic Matchmaking Protocol for Use in the Absence of a Continuously Available Third Party

  1986cited by this paper
• The Art of Computer Programming, Vol. 2

  1981cited by this paper

• No citing papers are available for this paper.