Context-Aware Differential Privacy for Language Modeling
This addresses privacy concerns for users of language models trained on sensitive data like emails and chat logs, representing an incremental improvement in privacy-preserving methods.
The paper tackles the problem of language models leaking sensitive training data by introducing Context-Aware Differentially Private Language Model (CADP-LM), which protects sensitive sentences and contexts to provide a highly accurate private model.
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security. A critical challenge pertains to how much information these models retain and leak about the training data. This is particularly urgent as the typical development of LMs relies on huge, often highly sensitive data, such as emails and chat logs. To contrast this shortcoming, this paper introduces Context-Aware Differentially Private Language Model (CADP-LM) , a privacy-preserving LM framework that relies on two key insights: First, it utilizes the notion of \emph{context} to define and audit the potentially sensitive information. Second, it adopts the notion of Differential Privacy to protect sensitive information and characterize the privacy leakage. A unique characteristic of CADP-LM is its ability to target the protection of sensitive sentences and contexts only, providing a highly accurate private model. Experiments on a variety of datasets and settings demonstrate these strengths of CADP-LM.