When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
Mixing real training images with T2I-generated synthetic data amplifies privacy leakage rather than reducing it — directly challenging a common GDPR mitigation assumption in European AI pipelines.
Summary written by editorial AI · Source link below
arXiv:2607.13541v1 Announce Type: new Abstract: To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image (T2I)-generated synthetic data, a paradigm we term Real-Synthetic Mix-Training (RSMT). While substituting synthetic data for sensitive real samples is widely regarded as a means to mitigate privacy exposure of the substituted data, the risk to the remaining real samples
Editorial Analysis
Enterprises using synthetic-data augmentation as a GDPR privacy safeguard may be unknowingly increasing re-identification risk, requiring revised data-protection impact assessments.
Re-examine any data-protection impact assessments that credit synthetic-data mixing as a privacy-enhancing measure and test for amplified memorisation.
Synthetic-data augmentation, widely assumed to protect privacy, may instead increase re-identification risk — a finding with direct GDPR implications.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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