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

Yanying Li, Wendy Hui Wang, Boxiang Dong

Research output: Contribution to journalConference article

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.

Original languageEnglish
Pages (from-to)77-79
Number of pages3
JournalCEUR Workshop Proceedings
Volume2335
StatePublished - 1 Jan 2019
Event2019 PAL: Privacy-Enhancing Artificial Intelligence and Language Technologies, PAL 2019 - Palo Alto, United States
Duration: 25 Mar 201927 Mar 2019

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