CoronaDef Workshop 2021
Sunday, 21 February
Serge Vaudenay, EPFL, Switzerland
Vikram Sharma Mailthody, James Wei, Nicholas Chen, Mohammad Behnia, Ruihao Yao, Qihao Wang, Vedant Agarwal, Churan He, Lijian Wang, Leihao Chen, Amit Agarwal, Edward Richter, Wen-mei Hwu, and Christopher Fletcher (University of Illinois at Urbana-Champaign); Jinjun Xiong (IBM); Andrew Miller and Sanjay Patel (University of Illinois at Urbana-Champaign)
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic and safely reopen society. Essential analytics tools such as contact tracing, super-spreader event detection, and exposure mapping require collecting and analyzing sensitive user information. The increasing use of such powerful data-driven applications necessitates a secure, privacy-preserving infrastructure for computation on personal data.
In this paper, we analyze two such computing infrastructures under development at the University of Illinois at Urbana-Champaign to track and mitigate the spread of COVID-19. First, we present Safer Illinois, a system for decentralized health analytics supporting two applications currently deployed with widespread adoption: digital contact tracing and COVID-19 status cards. Second, we introduce the RokWall architecture for privacy-preserving centralized data analytics on sensitive user data. We discuss the architecture of these systems, design choices, threat models considered, and the challenges we experienced in developing production-ready systems for sensitive data analysis.
We report on an independent assessment of the Android implementation of the Google/Apple Exposure Notification (GAEN) system. While many health authorities have committed to making the code for their contact tracing apps open source, these apps depend upon the GAEN API for their operation and this is not open source. Public documentation of the GAEN API is also limited. We find that the GAEN API uses a filtered Bluetooth LE signal strength measurement that can be potentially misleading with regard to the proximity between two handsets. We also find that the exposure duration values reported by the API are coarse grained and can somewhat overestimate the time that two handsets are in proximity. Updates to the GAEN API that can affect contact tracing performance, and so public health, are silently installed on user handsets. While facilitating rapid rollout of changes, the lack of transparency around this raises obvious concerns.
David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)
In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an ‘immuno-privileged’ class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens — randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.
Data Analytics and Expert Judgment in Time of Crisis: Experience Supporting FEMA/HHS COVID Task ForceIgor Linkov, PhD Senior Science and Technology Manager, US Army Engineer Research and Development Center; Senior Data Analyst (on detail), FEMA/HHS R1 COVID Task Force; Adjunct Professor, Carnegie Mellon University
Towards Defeating Mass Surveillance and SARS-CoV-2: The Pronto-C2 Fully Decentralized Automatic Contact Tracing SystemGennaro Avitabile, Vincenzo Botta, Vincenzo Iovino, and Ivan Visconti (University of Salerno)
Automatic contact tracing is currently used in several countries in order to limit the spread of SARS-CoV-2. Many governments decided to develop smartphone apps based on the “Exposure Notifications” designed by Apple and Google according to a decentralized approach previously proposed by the DP-3T team. Decentralization was pushed as a key feature to protect privacy in contrast to centralized approaches that could leverage automatic contact tracing to realize mass-surveillance programs.
In this work, taking into account the privacy and integrity vulnerabilities of DP-3T systems, we show the design of a decentralized contact tracing system named Pronto-C2 that has better resilience against various attacks. We also discuss the significant overhead of Pronto-C2 when used in real-world scenarios.
The COVID-19 pandemic created various new challenges for our societies. Quickly discovering new infections using automated contact tracing without endangering privacy of the general public is one of these. Most discussions concerning architectures for contact tracing applications revolved around centralized against decentralized approaches. In contrast, the system proposed in this work builds on the idea of message based contact tracing to inform users about their risk. Our main contribution is the combination of a blind-signature approach to verify infections with an anonymous postbox service. In our evaluation, we analyze all components in our system for performance and privacy, as well as security. We also derive parameters for operating our system in a pandemic scenario.
Benny Pinkas (Bar-Ilan University); Eyal Ronen (Tel Aviv University)
In recent months multiple proposals for contact tracing schemes for combating the spread of COVID-19 have been published. Many of those proposals try to implement this functionality in a decentralized and privacy-preserving manner using Bluetooth Low Energy (BLE).
In this paper, we present “Hashomer”, our proposal for a contact tracing scheme tailored for the Israeli Ministry of Health’s (MoH) “Hamagen” application. The design is fully decentralized, and has the following properties:
- Message Unlinkability — Different BLE messages sent by the same user cannot be linked to each other (except for messages sent by COVID-19 positive users who give consent to tracing their contacts, and only for messages sent within a short time period).
- Explainability — To convince users that they were exposed to a COVID-19 positive person, we let them learn the approximate time of contact. This also implies that users can potentially learn, using the phone’s GPS information, the location of the exposure.
- Partial Disclosure and Coercion Prevention — Users and the MoH are able to redact tracing information and exposure notifications for specific time intervals.
- Prevention of Relay Attacks – The design prevents attacks where a malicious receiver relays BLE transmissions from one location to other locations.
- Proof of exposure to a COVID-19 positive person — To prevent false reports about exposure, we allow users who are notified by the application about an exposure to a COVID-19 positive person, to prove this fact to the server.
- Identity Commitment — To prevent malicious changing or replacing keys, we bind the BLE messages to a unique ID in a privacy-preserving way.
- Performance — BLE payload size is limited to 16 bytes. The application uses only symmetric key cryptography (AES and HMAC). To reduce bandwidth, contact updates from the MoH are of limited size. Moreover, the local search for exposure is linear in the number of messages and number of COVID-19 positive persons
Tracy Tam, Asha Rao, and Joanne Hall (RMIT)
COVID19 has made small businesses around the world rapidly adopt new online sales channels and tools. In this digital push for survival, the cybersecurity of the new systems has likely been forgotten. An existing global cybersecurity skills shortage means traditional individualised security assessments for these newly digital businesses are not practical. This paper proposes a web based self-assessment system (SE-CAP) to enable small business owners to conduct their own cybersecurity assessments. Designed with rapid deployability in mind, SE-CAP uses proven web based technologies to deliver a new solution to help small businesses become cyber-safe. The design of SE-CAP takes into account small business issues around record keeping, time constraints and poor technical literacy. The generic nature of the system allows SE-CAP’s host organisation to customise and extend the self-assessment system beyond its initial scope. Challenges with industry cybersecurity knowledge gaps prevent SE-CAP’s completeness. However, these gaps could be filled, in the interim, by the host organisation.
Moderator: Ahmad-Reza Sadeghi, TU Darmstadt, Germany Panelists: Mario Guglielmetti, Legal Officer, European Data Protection Supervisor* Jaap-Henk Hoepman, Radbaud University, The Netherlands Alexandra Dmitrienko, University of Würzburg, Germany, Farinaz Koushanfar, UCSD, USA *attending in his personal capacity