PIVOT: Privacy-preserving outsourcing of text data for word embedding against frequency analysis attack

Authors

• Yanying Li, Charles V. Schaefer, Jr. School of Engineering and Science
• Wendy Hui Wang, Charles V. Schaefer, Jr. School of Engineering and Science
• Boxiang Dong, Montclair State UniversityFollow

Document Type

Conference Proceeding

Publication Date

1-1-2019

Journal / Book Title

Ceur Workshop Proceedings

Abstract

In this paper, we design PIVOT, a new privacy-preserving method that supports outsourcing of text data for word embedding. PIVOT includes a 1-to-many mapping function for text documents that can defend against the frequency analysis attack with provable guarantee, while preserving the word context during transformation.

Montclair State University Digital Commons Citation

Li, Yanying; Wang, Wendy Hui; and Dong, Boxiang, "PIVOT: Privacy-preserving outsourcing of text data for word embedding against frequency analysis attack" (2019). Department of Computer Science Faculty Scholarship and Creative Works. 764.
https://digitalcommons.montclair.edu/compusci-facpubs/764

Rights

Copyright © 2019 the authors.

Published Citation

Li, Y., Wang, W. H., & Dong, B. (2019). PIVOT: Privacy-preserving Outsourcing of Text Data for Word Embedding Against Frequency Analysis Attack. In CEUR Workshop Proceedings (Vol. 2335, pp. 77-79). CEUR-WS.