Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

As more and more pre-trained language models adopt on-cloud deployment, the privacy
issues grow quickly, mainly for the exposure of plain-text user data (eg, search history …

This position paper investigates the problem of automated text anonymisation, which is a
prerequisite for secure sharing of documents containing sensitive information about …

With the wide use of GPS enabled devices and Location-Based Services, location privacy
has become an increasingly worrying challenge to our community. Existing approaches …

Deep learning (DL) models for natural language processing (NLP) tasks often handle
private data, demanding protection against breaches and disclosures. Data protection laws …

We present a novel benchmark and associated evaluation metrics for assessing the
performance of text anonymization methods. Text anonymization, defined as the task of …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

With the increasing applications of language models, it has become crucial to protect these
models from leaking private information. Previous work has attempted to tackle this …

To protect the privacy of individuals whose data is being shared, it is of high importance to
develop methods allowing researchers and companies to release textual data while …