Detecting and Perturbing Privacy-Sensitive Neurons to Defend Embedding Inversion Attacks
Keywords: Text embedding, Defense Inversion Attack
Abstract: This paper introduces Defense through Perturbing Privacy Neurons (DPPN), a novel approach to protect text embeddings against inversion attacks. Unlike ex- isting methods that add noise to all embedding dimensions for general protection, DPPN identifies and perturbs only a small portion of privacy-sensitive neurons. We present a differentiable neuron mask learning framework to detect these neu- rons and a neuron-suppressing perturbation function for targeted noise injection. Experiments across six datasets show DPPN achieves superior privacy-utility trade- offs. Compared to baseline methods, DPPN reduces more privacy leakage by 5-78% while improving downstream task performance by 14-40%. Tests on real- world sensitive datasets demonstrate DPPN’s effectiveness in mitigating sensitive information leakage to 17%, while baseline methods reduce it only to 43%.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 13525
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