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Encyclopedia of Big Data Technologies pp 1067–1073Cite as

Keyword Attacks and Privacy Preserving in Public-Key-Based Searchable Encryption

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Definitions

Keyword privacy, Keyword-guessing attacks, Searchable encryption, Public key.

Overview

The notion of public key encryption with keyword search (PEKS) provides a flexible and efficient approach to retrieve encrypted data stored in a remote server. A main threat in PEKS is the keyword guessing attack, where disclosed keywords leak the associated data and the user’s interest. This gives rise to that the keyword privacy becomes a concern in searchable encryption. In this paper, we present a brief history of keyword privacy in searchable encryption. We make a systematic investigation of keyword privacy issue in public key-based searchable encryption. We describe keyword guessing attacks in a general PEKS scheme. According to different attackers, we refine inherent reasons and summarize enhancement achievements. This preserves the keyword privacy and provides guidance for the secure development of searchable encryption.

Introduction

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Author information

Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Peng Jiang

  2. Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Fuchun Guo & Willy Susilo

  3. Department of Computer Science, Surrey Centre for Cyber Security, University of Surrey, Guildford, UK

    Jinguang Han

Authors
  1. Peng Jiang
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  2. Fuchun Guo
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  3. Willy Susilo
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  4. Jinguang Han
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Corresponding author

Correspondence to Willy Susilo .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Jiang, P., Guo, F., Susilo, W., Han, J. (2019). Keyword Attacks and Privacy Preserving in Public-Key-Based Searchable Encryption. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_232

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