A Privacy-Preserving Approach to Health Insurance Fraud Detection Using Vertical Federated Learning.

Raghi K R, Arjun Paramarthalingam, Harini Kanthan, Mahalakshmi Karthiban

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

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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

4 authors.

Raghi K RComputer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, TN, India.ORCID 0000-0002-0333-3033

Arjun ParamarthalingamComputer Science and Engineering, University College of Engineering Villupuram, Villupuram 605103, TN, India.ORCID 0000-0003-3998-0221

Harini KanthanComputer Science and Engineering, University College of Engineering Villupuram, Villupuram 605103, TN, India.

Mahalakshmi KarthibanComputer Science and Engineering, University College of Engineering Villupuram, Villupuram 605103, TN, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In fraud detection, centralized approaches often face challenges related to data protection, security, and potential data breaches. Such methods require sensitive healthcare and insurance data to be pooled in one location, which increases vulnerability to misuse. This paper introduces FraudNetX, a privacy-preserving fraud detection framework, by utilizing Vertical Federated Learning (VFL) to address centralized system limitations. VFL enables models to be trained collaboratively while ensuring data privacy and security through quantifiable Differential Privacy (DP) guarantees (ε = 1.0, δ = 1 × 10

Indexed as

FraudInsurance, HealthPrivacyAlgorithmsComputer SecurityConfidentialityFederated LearningHumansMachine LearningNeural Networks, Computerhybrid neural network modelprivacy preserving fraud detectionsecure data sharingvertical federated learning

Identifiers

PMID41374728

DOI10.3390/s25237354

PMCPMC12693874

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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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.