Public Goods From Private Data -- An Efficacy and Justification Paradox
for Digital Contact Tracing
- URL: http://arxiv.org/abs/2007.07016v1
- Date: Tue, 14 Jul 2020 13:08:29 GMT
- Title: Public Goods From Private Data -- An Efficacy and Justification Paradox
for Digital Contact Tracing
- Authors: Andrew Buzzell
- Abstract summary: Privacy-centric analysis treats data as private property, frames the relationship between individuals and governments as adversarial and entrenches technology platforms as gatekeepers.
To overcome the barriers to ethical and effective DCT, and develop infrastructure and policy that supports the realization of potential public benefits of digital technology, a public resource conception of aggregate data should be developed.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Debate about the adoption of digital contact tracing (DCT) apps to control
the spread of COVID-19 has focussed on risks to individual privacy (Sharma &
Bashir 2020, Tang 2020). This emphasis reveals significant challenges to
ethical deployment of DCT, but generates constraints which undermine
justification to implement DCT. It would be a mistake to view this result
solely as the successful operation of ethical foresight analysis (Floridi &
Strait 2020), preventing deployment of potentially harmful technology.
Privacy-centric analysis treats data as private property, frames the
relationship between individuals and governments as adversarial, entrenches
technology platforms as gatekeepers, and supports a conception of emergency
public health authority as limited by individual consent and considerable
corporate influence that is in some tension with the more communitarian values
that typically inform public health ethics. To overcome the barriers to ethical
and effective DCT, and develop infrastructure and policy that supports the
realization of potential public benefits of digital technology, a public
resource conception of aggregate data should be developed.
Related papers
- 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) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Operationalizing Digital Self Determination [0.0]
We live in an era of datafication, in which life is increasingly quantified and transformed into intelligence for private or public benefit.
Existing methods to limit asymmetries (e.g., consent) have limitations to adequately address the challenges at hand.
A new principle and practice of digital self-determination (DSD) is therefore required.
arXiv Detail & Related papers (2022-11-15T22:28:51Z) - Public Health, Technology, and Human Rights: Lessons from Digital
Contact Tracing [0.0]
Digital Contact Tracing and Exposure Notifications Systems were developed for use as public-interest technologies during the SARS-CoV-2 global pandemic.
This paper will highlight the importance of upholding the principles of Scientific Validity, Necessity, Time Boundedness, and Proportionality.
arXiv Detail & Related papers (2021-07-15T18:31:04Z) - 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) - DHP Framework: Digital Health Passports Using Blockchain -- Use case on
international tourism during the COVID-19 pandemic [0.0]
Digital Contact Tracing is not suitable for proactively preventing the spread of a disease.
We discuss the concept of a Health Passport as a means of verifying that individuals are disease risk-free.
We present the DHP Framework that uses a private blockchain and Proof of Authority for issuing Digital Health Passports.
arXiv Detail & Related papers (2020-05-18T17:50:41Z) - 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 tools against COVID-19: Framing the ethical challenges and how
to address them [3.1498833540989413]
We present a typology of the primary digital public health applications currently in use.
For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns.
We propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools.
arXiv Detail & Related papers (2020-04-21T18:39:08Z) - 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.