{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:13:20Z","timestamp":1772118800515,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"dtec.bw\u2014Digitalization and Technology Research Center of the Bundeswehr"},{"name":"European Union\u2014NextGenerationEU"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Profile images from social networks are a valuable source of data for AI analytics, but they contain biometric identifiers that pose serious privacy risks. The current face anonymization techniques often destroy semantic information, and generative de-identification methods are vulnerable to re-identification attacks. In this paper, we propose a template-driven multimodal face pseudonymization framework that allows for the privacy-preserving analysis of facial image data while retaining analytically relevant attributes. Our approach uses a FaceNet-based CelebA attribute classifier to extract fine-grained facial attributes and a DeepFace model to extract high-level demographic attributes. Rather than relying on stochastic large language models, we introduce deterministic template-based attribute-to-text conversion to ensure consistency and reproducibility and prevent unintended attribute hallucination. The resulting textual description serves as the sole conditioning input for Janus-Pro, a multimodal text-to-image generation model that synthesizes realistic yet non-identifiable face images. We evaluate our method on the CelebA dataset under a strong adversarial threat model, employing state-of-the-art face recognition systems to assess re-identification and linkability attacks. Our results demonstrate a substantial reduction in identity leakage while preserving semantic attributes.<\/jats:p>","DOI":"10.3390\/a19030176","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:58:03Z","timestamp":1772114283000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Template-Driven Multimodal Face Pseudonymization for Privacy-Preserving Big Data Analytics"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4223-2881","authenticated-orcid":false,"given":"Yeong Su","family":"Lee","sequence":"first","affiliation":[{"name":"Research Institute CODE, University of the Bundeswehr Munich, 85579 Neubiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9310-6450","authenticated-orcid":false,"given":"Hendrik","family":"Bothe","sequence":"additional","affiliation":[{"name":"Research Institute CODE, University of the Bundeswehr Munich, 85579 Neubiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8180-5606","authenticated-orcid":false,"given":"Michaela","family":"Geierhos","sequence":"additional","affiliation":[{"name":"Research Institute CODE, University of the Bundeswehr Munich, 85579 Neubiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","article-title":"Big Data: A Survey","volume":"19","author":"Chen","year":"2014","journal-title":"Mob. 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