Contact Tracing Made Un-relay-able

• URL: http://arxiv.org/abs/2010.12641v2
• Date: Mon, 2 Nov 2020 16:56:56 GMT
• Title: Contact Tracing Made Un-relay-able
• Authors: Marco Casagrande, Mauro Conti, Eleonora Losiouk
• Abstract summary: SARS-CoV-2 pandemic put a heavy strain on the healthcare system of many countries. Governments chose different approaches to face the spread of the virus. Mobile apps allow to achieve a privacy-preserving contact tracing of citizens.
• Score: 18.841230080121118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated contact tracing is a key solution to control the spread of airborne transmittable diseases: it traces contacts among individuals in order to alert people about their potential risk of being infected. The current SARS-CoV-2 pandemic put a heavy strain on the healthcare system of many countries. Governments chose different approaches to face the spread of the virus and the contact tracing apps were considered the most effective ones. In particular, by leveraging on the Bluetooth Low-Energy technology, mobile apps allow to achieve a privacy-preserving contact tracing of citizens. While researchers proposed several contact tracing approaches, each government developed its own national contact tracing app. In this paper, we demonstrate that many popular contact tracing apps (e.g., the ones promoted by the Italian, French, Swiss government) are vulnerable to relay attacks. Through such attacks people might get misleadingly diagnosed as positive to SARS-CoV-2, thus being enforced to quarantine and eventually leading to a breakdown of the healthcare system. To tackle this vulnerability, we propose a novel and lightweight solution that prevents relay attacks, while providing the same privacy-preserving features as the current approaches. To evaluate the feasibility of both the relay attack and our novel defence mechanism, we developed a proof of concept against the Italian contact tracing app (i.e., Immuni). The design of our defence allows it to be integrated into any contact tracing app.

Related papers

• Protect Your Score: Contact Tracing With Differential Privacy Guarantees [68.53998103087508]
  We argue that privacy concerns currently hold deployment back. We propose a contact tracing algorithm with differential privacy guarantees against this attack. Especially for realistic test scenarios, we achieve a two to ten-fold reduction in the infection rate of the virus.
  arXiv  Detail & Related papers  (2023-12-18T11:16:33Z)
• CoAvoid: Secure, Privacy-Preserved Tracing of Contacts for Infectious Diseases [25.014640577594566]
  This paper proposes CoAvoid, a decentralized, privacy-preserved contact tracing system. CoAvoid leverages the Google/Apple Exposure Notification (GAEN) API to achieve decent device compatibility and operating efficiency. Compared with four state-of-art contact tracing applications, CoAvoid can reduce upload data by at least 90% and simultaneously resist wormhole and replay attacks.
  arXiv  Detail & Related papers  (2022-01-20T12:19:21Z)
• Explainable Link Prediction for Privacy-Preserving Contact Tracing [5.866574931696403]
  Contact tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. We present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.
  arXiv  Detail & Related papers  (2020-12-10T08:58:24Z)
• Predicting Infectiousness for Proactive Contact Tracing [75.62186539860787]
  Large-scale digital contact tracing is a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. This paper develops and test methods that can be deployed to a smartphone to proactively predict an individual's infectiousness.
  arXiv  Detail & Related papers  (2020-10-23T17:06:07Z)
• Report prepared by the Montreal AI Ethics Institute In Response to Mila's Proposal for a Contact Tracing App [0.0]
  "COVI" is the name of a recent contact tracing app developed by Mila. The app was designed to inform each individual of their relative risk of being infected with the virus. This article will discuss: the extent to which diversity has been considered in the design of the app, assumptions surrounding users' interaction with the app, as well as unanswered questions surrounding transparency, accountability, and security.
  arXiv  Detail & Related papers  (2020-08-11T06:05:13Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)
• Decentralized Privacy-Preserving Proximity Tracing [50.27258414960402]
  DP3T provides a technological foundation to help slow the spread of SARS-CoV-2. System aims to minimise privacy and security risks for individuals and communities.
  arXiv  Detail & Related papers  (2020-05-25T12:32:02Z)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)
• Give more data, awareness and control to individual citizens, and they will help COVID-19 containment [74.10257867142049]
  Contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance. We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
  arXiv  Detail & Related papers  (2020-04-10T20:30:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.