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de la Cuadra Lozano, M., Quille, K., Pugh, J., & Nolan, M. (2024). Assessing the Impact of Privacy-Preserving Machine Learning and Bias Introduction on Data Anonymisation. In Proceedings of the 2024 Conference on Human Centred Artificial Intelligence—Education and Practice (pp. 56-56). ACM.
https://doi.org/10.1145/3701268.3701283

has been cited by the following article:

• TITLE: Anonymity in the Age of AI

  AUTHORS: Parisasadat Shojaei, Nabi Zameni, Rezza Moieni

  KEYWORDS: Anonymity, Privacy-Enhancing Technologies, Differential Privacy, Federated Learning, Homomorphic Encryption, GDPR, Re-Identification, AI Privacy

  JOURNAL NAME: Open Journal of Social Sciences, Vol.13 No.8, August 5, 2025

  ABSTRACT: Artificial intelligence (AI) is eroding traditional de-identification practices by enabling accurate re-identification of images, text and behavioural traces. A systematic review of 64 peer-reviewed studies published between 2013 and 2025—47 on technical privacy-enhancing technologies (PETs) and 17 on the EU General Data Protection Regulation (GDPR)—shows that no single safeguard withstands modern adversaries. The most resilient configurations layer differential privacy, federated learning and partial homomorphic encryption, maintaining