scholar.google.com › citations
Oct 20, 2019 · This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dx-privacy designed to achieve ...
ABSTRACT. Accurately learning from user data while providing quanti able pri- vacy guarantees provides an opportunity to build better ML models.
Jan 22, 2020 · This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of d_χ-privacy designed to achieve ...
ABSTRACT. Accurately learning from user data while providing quantifiable pri- vacy guarantees provides an opportunity to build better ML models.
A formal approach to carrying out privacy preserving text perturbation using the notion of d -privacy designed to achieve geo-indistinguishability in ...
Oct 20, 2019 · We provide a formal approach to carrying out intent preserving text perturbation backed up by formal privacy analysis (Sec. 2). 2. We provide a ...
Privacy-Preserving Textual Analysis via Calibrated Perturbations ; Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing - ...
This talk presents a formal approach to carrying out privacy preserving text perturbation using a variant of Differential Privacy (DP) known as Metric DP ...
A solution that helps apply a privacy preserving mechanism to NLP data, using Amazon SageMaker. - sagemaker-privacy-for-nlp/README.
Oluwaseyi Feyisetan, Borja Balle: Privacy-Preserving Textual Analysis via Calibrated Perturbations. PrivateNLP@WSDM 2020: 41-42.