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Peer Reviews (247)

Published on in Vol 6 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72525, first published .
Peer Review of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

Peer Review of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

Peer Review of “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection”

Authors of this article:

Trutz Bommhardt1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

    Related ArticlesPreprint (arXiv) https://arxiv.org/abs/2410.17459v1
    Authors' Response to Peer-Review Reports https://med.jmirx.org/2025/1/e72527
    Published Article https://med.jmirx.org/2025/1/e70100
    JMIRx Med 2025;6:e72525

    doi:10.2196/72525

    Keywords

    privacy-preserving AI; latent space projection; data obfuscation; AI Governance; machine learning privacy; differential privacy; k-anonymity; HIPAA; GDPR; compliance; data utility; privacy-utility trade-off; responsible AI; medical imaging privacy; secure data sharing; LSP; artificial intelligence 

    This is a peer-review report for “Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection.”


    General Comments

    I thoroughly enjoyed reading this paper [1] as it is a well-written article that will make an important contribution to the literature on the development of privacy-preserving artificial intelligence (AI) governance. I have attached a few comments to improve the study.

    Specific Comments

    Major Comments

    Something like a discussion that embeds the latent space projection for AI governance and the results in the current scientific debate is missing before or after Chapter VII.

    Minor Comments

    In Chapter II B (Existing privacy-preserving techniques), please provide some further sources to demonstrate that the challenges mentioned are still relevant, as some sources are relatively old (eg, from 2009).

    Conflicts of Interest

    None declared.

    1. Vaijainthymala Krishnamoorthy M. Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection. JMIRx Med. 2025;6:e70100. [CrossRef]

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    AI: artificial intelligence

    Edited by Ching Nam Hang; This is a non–peer-reviewed article. submitted 11.02.25; accepted 11.02.25; published 12.03.25.

    Copyright

    © Trutz Bommhardt. Originally published in JMIRx Med (https://med.jmirx.org), 12.3.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.