Interpretable Privacy Preservation of Text Representations Using Vector Steganography
This addresses privacy risks in language models for users and organizations handling sensitive text data, but it is incremental as it builds on existing efforts to minimize these risks.
The research tackles the problem of adversaries exploiting spurious associations in language model representations to infer private attributes, aiming to develop interpretable privacy preservation methods that retain data utility and guarantee privacy.
Contextual word representations generated by language models (LMs) learn spurious associations present in the training corpora. Recent findings reveal that adversaries can exploit these associations to reverse-engineer the private attributes of entities mentioned within the corpora. These findings have led to efforts towards minimizing the privacy risks of language models. However, existing approaches lack interpretability, compromise on data utility and fail to provide privacy guarantees. Thus, the goal of my doctoral research is to develop interpretable approaches towards privacy preservation of text representations that retain data utility while guaranteeing privacy. To this end, I aim to study and develop methods to incorporate steganographic modifications within the vector geometry to obfuscate underlying spurious associations and preserve the distributional semantic properties learnt during training.