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.

该论文旨在研究和开发可解释的方法，将隐写术方法与向量几何相结合，以混淆潜在的虚假关联，并保留训练期间学习到的分布语义属性，以实现文本表示数据隐私保护。