Privacy-Preserving Face Recognition Using Noised Eigenvectors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200951" target="_blank" >RIV/00216305:26220/26:0200951 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Privacy-Preserving Face Recognition Using Noised Eigenvectors

Popis výsledku v původním jazyce

Widespread face recognition systems raise significant privacy concerns due to potential data exposure, especially with centralized data storage. We propose a privacy-preserving framework integrating k-same pixelation, Principal Component Analysis (PCA), and Differential Privacy (DP). Our pipeline applies k-same smoothing for initial feature averaging, uses PCA for dimensionality reduction while preserving essential facial features, and adds Laplace noise to the resulting projection vectors to achieve DP. This method masks biometric information, operating efficiently in the lower-dimensional PCA space, aiming to balance privacy protection with the utility needed for identity verification. Evaluations on the LFW dataset quantitatively analyze this trade-off using MSE and SSIM metrics. Results confirm integrating DP enhances privacy. Crucially, experiments show adding noise to lower-dimensional projection vectors preserves utility better than noising higher-dimensional eigenfaces. We identified parameters (k=10, PCA ratio=0.19, $epsilon$n=0.24) yielding a practical balance (Avg. MSE 1499, Avg. SSIM 0.38), enabling effective machine recognition on the anonymized data and demonstrating the framework's viability.

Název v anglickém jazyce

Privacy-Preserving Face Recognition Using Noised Eigenvectors

Popis výsledku anglicky

Widespread face recognition systems raise significant privacy concerns due to potential data exposure, especially with centralized data storage. We propose a privacy-preserving framework integrating k-same pixelation, Principal Component Analysis (PCA), and Differential Privacy (DP). Our pipeline applies k-same smoothing for initial feature averaging, uses PCA for dimensionality reduction while preserving essential facial features, and adds Laplace noise to the resulting projection vectors to achieve DP. This method masks biometric information, operating efficiently in the lower-dimensional PCA space, aiming to balance privacy protection with the utility needed for identity verification. Evaluations on the LFW dataset quantitatively analyze this trade-off using MSE and SSIM metrics. Results confirm integrating DP enhances privacy. Crucially, experiments show adding noise to lower-dimensional projection vectors preserves utility better than noising higher-dimensional eigenfaces. We identified parameters (k=10, PCA ratio=0.19, $epsilon$n=0.24) yielding a practical balance (Avg. MSE 1499, Avg. SSIM 0.38), enabling effective machine recognition on the anonymized data and demonstrating the framework's viability.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů