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 …

Cloud computing is a long‐standing dream of computing as a utility, where users can store
their data remotely in the cloud to enjoy on‐demand services and high‐quality applications …

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

The increasing volume of personal and sensitive data being harvested by data controllers
makes it increasingly necessary to use the cloud not just to store the data, but also to …

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

Large language models (LLMs) have become the state of the art in natural language
processing. The massive adoption of generative LLMs and the capabilities they have shown …

Accurately learning from user data while providing quantifiable privacy guarantees provides
an opportunity to build better ML models while maintaining user trust. This paper presents a …

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems
utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It …

We address the problem of how to “obfuscate” texts by removing stylistic clues which can
identify authorship, whilst preserving (as much as possible) the content of the text. In this …

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the
challenges faced by cultural heritage institutions in the digital age, which have led to the …