A LWE-Based Receiver-Deniable Encryption Scheme

Po Wen Chi*, Ming Hung Wang, Yun Hsin Chuang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this era, user privacy is a very important issue and encryption is a general solution. However, the encryption technique cannot keep user privacy while the key is leaked under outside coercion. To solve this problem, the deniable encryption technique is a useful tool since it makes the user be able to answer the outside coercer with fake data and proofs. In this paper, we propose a deniable encryption scheme based on the LWE problem (Learning with Errors). We enhance the Regev LWE encryption scheme proposed in 2009 by increasing the probability of decryption failure. So the user can cheat the outsider with the fake message. We also apply the multi-distributional concept proposed by O’Neill et al. scheme to construct our deniable encryption scheme.

Original languageEnglish
Title of host publication2021 International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications
EditorsGeorge A. Tsihrintzis, Shiuh-Jeng Wang, Iuon-Chang Lin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages124-133
Number of pages10
ISBN (Print)9783031054907
DOIs
Publication statusPublished - 2023
Event1st International Conference on Security and Information Technologies with AI, Internet Computing, and Big data Applications, SITAIBA 2021 - Taichung, Taiwan
Duration: 2021 Nov 182021 Nov 20

Publication series

NameSmart Innovation, Systems and Technologies
Volume314
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference1st International Conference on Security and Information Technologies with AI, Internet Computing, and Big data Applications, SITAIBA 2021
Country/TerritoryTaiwan
CityTaichung
Period2021/11/182021/11/20

Keywords

  • Deniable encryption
  • Learning with errors
  • Non-committing encryption

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science

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