The widespread use of pre-trained language models has revolutionised knowledge transfer in natural language processing tasks. However, there is a concern regarding potential breaches of user trust due to the risk of re-identification attacks, where malicious users could extract Personally Identifiable Information (PII) from other datasets. To assess the extent of extractable personal information on popular pre-trained models, we conduct the first wide coverage evaluation and comparison of state-of-the-art privacy-preserving algorithms on a large multi-lingual dataset for sentiment analysis annotated with demographic information (including location, age, and gender). Our results suggest a link between model complexity, pre-training data volume, and the efficacy of privacy-preserving embeddings. We found that privacy-preserving methods demonstrate greater effectiveness when applied to larger and more complex models, with improvements exceeding > 20 % over non-private baselines. Additionally, we observe that local differential privacy imposes serious performance penalties of ≈ 20 % in our test setting, which can be mitigated using hybrid or metric-DP techniques. • Compares privacy-preservation techniques across popular language models. • Analysis demonstrates significant reduction in relative attack success. • Uses a multilingual corpus, illustrating results on diverse European languages. • Includes both high-, medium-, and lower-resource languages. • To our knowledge, this is the first study to extend this analysis beyond English. • Expands on previous work (Plant et al., 2021) with MDP and CGT techniques.

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others.However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and context cues that may lead to privacy leaks.To ameliorate this issue, we propose Context-Aware Private Embeddings (CAPE), a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings.Specifically, CAPE firstly applies calibrated noise through differential privacy to maintain the privacy of text representations by preserving the encoded semantic links while obscuring sensitive information.Next, CAPE employs an adversarial training regime that obscures identified private variables.Experimental results demonstrate that our proposed approach is more effective in reducing private information leakage than either single intervention, with approximately a 3% reduction in attacker performance compared to the best-performing current method.