Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

• URL: http://arxiv.org/abs/2010.05514v2
• Date: Fri, 13 Nov 2020 08:59:54 GMT
• Title: Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications
• Authors: Luca Bedogni, Shakila Khan Rumi, Flora Salim
• Abstract summary: We show that it is possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario.
• Score: 3.390388295995944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack.

Related papers

• Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data [7.237822612572237]
  We introduce a new Infectious Path Centrality network metric to identify important transmission events. We also explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%.
  arXiv  Detail & Related papers  (2025-01-30T17:57:15Z)
• DNA: Differentially private Neural Augmentation for contact tracing [62.740950398187664]
  Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early. We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing. This work marks an important first step in integrating deep learning into contact tracing while maintaining essential privacy guarantees.
  arXiv  Detail & Related papers  (2024-04-20T13:43:28Z)
• On the Privacy and Integrity Risks of Contact-Tracing Applications [32.01611421032163]
  Smartphone-based contact-tracing applications are at the epicenter of the global fight against the Covid-19 pandemic. This paper describes two important attacks that affect a broad swath of contact-tracing applications.
  arXiv  Detail & Related papers  (2020-12-06T15:05:02Z)
• 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)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)
• An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data from Mobile Cellular Networks [5.409709616786615]
  We propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications.
  arXiv  Detail & Related papers  (2020-07-06T11:40:23Z)
• 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)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• 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)

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.