Authors : Onyia Ogochukwu Sophia; Akawuku Mirian Ogheneyovwino; Chekwube Georgina Nwankwo

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/2s6kbc9f

DOI : https://doi.org/10.38124/ijisrt/26sep797

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Digital services increasingly depend on information generated from users’ online activities, yet the same traces that make these services useful can expose individuals to re-identification and inference. This study develops and evaluates a context-aware Hybrid Local Differential Privacy–Federated Learning (LDP-FL) approach for protecting digital footprints while retaining useful machine-learning performance. The work distinguishes digital footprints into active, passive and hybrid categories and assigns privacy protection according to feature sensitivity rather than applying a single noise level to every attribute. The proposed pipeline perturbs sensitive features locally with a Laplace mechanism, clips local gradients, and uses Federated Averaging to train a global model without centralising raw client data.

Keywords : Digital Footprint; Local Differential Privacy; Federated Learning; Privacy-Preserving Machine Learning; ReIdentification; Privacy–Utility Trade-Off; Context-Aware Privacy.

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