DNA: Differentially private Neural Augmentation for contact tracing
Updated 3 h ago · first seen 11 Sept 2026
paper_01M294FRPP9XXQ3XQ4T3JS5N4P
- Published
- 11 Sept 2026
- T1 · 3 h ago
- arXiv
- 2404.13381
- T1 · 3 h ago
- Category
- cs.LG
- T1 · 3 h ago
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
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
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
Conflicts
None
No models linked to this paper yet.
- Authors
- Rob Romijnders, Christos Louizos, Yuki M. Asano
As of
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 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. | → 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 →
New paper: DNA: Differentially private Neural Augmentation for contact tracing
arxiv
| 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.