Evidence map›Paper›PMID 38259959›Full record

ArticleProceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium2023

Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features.

Zixiao Zong, Mengwei Yang, Justin Ley, Carter T Butts, Athina Markopoulou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zixiao ZongUniversity of California, Irvine, Irvine, CA, USA.
Mengwei YangUniversity of California, Irvine, Irvine, CA, USA.
Justin LeyUniversity of California, Irvine, Irvine, CA, USA.
Carter T ButtsUniversity of California, Irvine, Irvine, CA, USA.
Athina MarkopoulouUniversity of California, Irvine, Irvine, CA, USA.

Funding

DMS/NIGMS 2: Statistical Network Models for Protein AggregationR01GM144964 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI BUTTS, CARTER TRIBLEY · 2021 to 2024
$1.2M
NIGMS NIH HHS R01 GM144964
6 · The paper itself

Abstract

We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a

Indexed as

Federated AnalyticsKernel Density Estimation (KDE)Population ModelingPrivacyRandom Fourier Features

Identifiers

PMID38259959
PMCPMC10803056

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.