Scientific News Report

𝗣𝗿𝗶𝘃𝗮𝗰𝘆-𝗣𝗿𝗲𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗩𝗶𝘀𝘂𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗨𝘀𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗦𝗶𝗻𝗴𝗹𝗲-𝗩𝗮𝗹𝘂𝗲𝗱 𝗡𝗲𝘂𝘁𝗿𝗼𝘀𝗼𝗽𝗵𝗶𝗰 𝗦𝗲𝘁𝘀 𝗮𝗻𝗱 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗲𝗱 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻

August 26, 2026   Mr. E.O. Adamu

𝗣𝗿𝗶𝘃𝗮𝗰𝘆-𝗣𝗿𝗲𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗩𝗶𝘀𝘂𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗨𝘀𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗦𝗶𝗻𝗴𝗹𝗲-𝗩𝗮𝗹𝘂𝗲𝗱 𝗡𝗲𝘂𝘁𝗿𝗼𝘀𝗼𝗽𝗵𝗶𝗰 𝗦𝗲𝘁𝘀 𝗮𝗻𝗱 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗲𝗱 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻
Scientific News Report

Can high-dimensional data be analysed visually without exposing the original sensitive values?

This study introduces a new concept of complex single-valued neutrosophic sets and applies it to privacy-preserving visual analytics for fully encrypted high-dimensional data.

The proposed framework is designed to allow important structural and relational information to remain analysable even when the original numerical data are hidden through encryption.

The researchers developed an encrypted analytical approach based on cotangent similarity, dimensionality reduction, and visualisation.

The framework uses three-dimensional encrypted parallel coordinates, 3D t-distributed stochastic neighbour embedding, and encrypted Pearson correlation heatmaps to examine relationships within encrypted data.

The results showed that the encrypted visualisations were able to preserve the main similarity patterns of the original data.

Only small, controlled changes were introduced into the best-matching pairs, variance distribution, class relationships, and correlation structures.

The small differences observed in variance and normalised similarity values suggest that the encryption process had little effect on the underlying analytical structure of the data.

This means that important geometric and relational characteristics can still be studied without revealing the original feature values.

The study demonstrates that encrypted data can remain useful for similarity analysis, dimensionality reduction, clustering, and visual exploration while maintaining privacy.

The proposed approach therefore offers a way to balance analytical usefulness with data confidentiality in situations where sensitive information cannot be exposed directly.

The authors conclude that complex single-valued neutrosophic sets combined with encrypted visual analytics can support secure similarity analysis and privacy-preserving decision-support applications while retaining analytical accuracy.

📖 Read the full article here:
https://doi.org/10.46481/jnsps.2026.3254

Published in: Journal of the Nigerian Society of Physical Sciences