Autoencoder-Enhanced Hierarchical Mondrian Anonymization via Latent Representations.

Junpeng Hu, Tao Hu, Zhenwu Xu, Jinan Shen, Minghui Zheng

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2026. 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.

Junpeng HuSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China.

Tao HuCollege of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China.

Zhenwu XuSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China.

Jinan ShenCollege of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China.

Minghui ZhengSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Releasing structured microdata requires balancing utility and privacy under group-based disclosure risks. We propose AE-LRHMA, a hybrid anonymization framework that performs Mondrian-style hierarchical partitioning in an autoencoder-learned latent space and integrates local (

Indexed as

autoencoderdata anonymizationk-anonymitylatent space partitioninglinkage-attack riskmicroaggregation

Identifiers

PMID42072498

DOI10.3390/e28040372

PMCPMC13114924

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.