How to dp-fy ml: A practical guide to machine learning with differential privacy
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 …
constant focus of research. Modern ML models have become more complex, deeper, and …
A survey on differential privacy for unstructured data content
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 …
generated and shared, and it is a challenge to protect sensitive personal information in …
The-x: Privacy-preserving transformer inference with homomorphic encryption
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 …
issues grow quickly, mainly for the exposure of plain-text user data (eg, search history …
Anonymisation models for text data: State of the art, challenges and future directions
This position paper investigates the problem of automated text anonymisation, which is a
prerequisite for secure sharing of documents containing sensitive information about …
prerequisite for secure sharing of documents containing sensitive information about …
Vector-indistinguishability: location dependency based privacy protection for successive location data
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 …
has become an increasingly worrying challenge to our community. Existing approaches …
[HTML][HTML] How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing
Deep learning (DL) models for natural language processing (NLP) tasks often handle
private data, demanding protection against breaches and disclosures. Data protection laws …
private data, demanding protection against breaches and disclosures. Data protection laws …
The text anonymization benchmark (tab): A dedicated corpus and evaluation framework for text anonymization
We present a novel benchmark and associated evaluation metrics for assessing the
performance of text anonymization methods. Text anonymization, defined as the task of …
performance of text anonymization methods. Text anonymization, defined as the task of …
Sok: differential privacies
D Desfontaines, B Pejó - arXiv preprint arXiv:1906.01337, 2019 - arxiv.org
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 …
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …
Selective differential privacy for language modeling
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 …
models from leaking private information. Previous work has attempted to tackle this …
Differentially private language models for secure data sharing
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 …
develop methods allowing researchers and companies to release textual data while …