User-Entity Differential Privacy in Learning Natural Language Models

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs). To preserve UeDP, we developed a novel algorithm, called UeDP-Alg, optimizing the trade-off between privacy loss and model utility with a tight sensitivity bound derived from seamlessly combining user and sensitive entity sampling processes. An extensive theoretical analysis and evaluation show that our UeDP-Alg outperforms baseline approaches in model utility under the same privacy budget consumption on several NLM tasks, using benchmark datasets.

Identifier

85147902940 (Scopus)

ISBN

[9781665480451]

Publication Title

Proceedings 2022 IEEE International Conference on Big Data Big Data 2022

External Full Text Location

https://doi.org/10.1109/BigData55660.2022.10020247

First Page

1465

Last Page

1474

Grant

CNS-1850094

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS