ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Differentially Private Distribution Estimation Using Functional Approximation.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2025Article
- Privacy-Preserving Visualization of Brain Functional Connectivity.bioRxiv : the preprint server for biology · 2024Article
- Federated Privacy-Preserving Visualization: A Vision Paper.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The connectogram is a commonly used visualization of brain functional network connectivity (FNC). In this paper we study the problem of privacy-preserving connectogram visualization using differential privacy. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. In order to obtain a better privacy/visual utility tradeoff, we propose a new workflow for connectogram visualization with privacy guarantees. This workflow successfully generates connectograms similar to their non-private counterparts for group comparisons. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization for biomedical data.
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Registered trials
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.