DNA: Differentially private Neural Augmentation for contact tracing — Paper

Abstract

The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early. However, this was not generally adopted in the recent pandemic, and privacy concerns are cited as the most important reason. We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing. Whereas previous work was based on statistical inference only, we augment the inference with a learned neural network and ensure that this neural augmentation satisfies differential privacy. In a simulator for COVID19, even at epsilon=1 per message, this can significantly improve the detection of potentially infected individuals and, as a result of targeted testing, reduce infection rates. This work marks an important first step in integrating deep learning into contact tracing while maintaining essential privacy guarantees.

Authors 4

Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

Specification

Official page: arxiv.org/abs/2404.13381
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
arXiv id: 2404.13381
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Categories: cs.LG, cs.CR, cs.MA, q-bio.PE
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
PDF: arxiv.org/pdf/2404.13381
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

3 h ago

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None

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Authors: Rob Romijnders, Christos Louizos, Yuki M. Asano

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Claim history for Categories
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.LG, cs.CR, cs.MA, q-bio.PE	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDNA: Differentially private Neural Augmentation for contact tracing
New paper: DNA: Differentially private Neural Augmentation for contact tracing
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	53 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.