4.7 Article

Lattice-based public key searchable encryption with fine-grained access control for edge computing

Publisher

ELSEVIER
DOI: 10.1016/j.future.2021.09.012

Keywords

Edge computing; Searchable encryption; Post-quantum security; Learning with errors

Funding

  1. National Natural Science Foundation of China [62072062, U20A20176]
  2. Natural Science Foundation of Chongqing, China [cstc2019jcyjjqX0026]

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Edge computing serves as a bridge between cloud computing platforms and IoT devices, providing on-demand data services to reduce latency and network congestion. Data security in edge computing can be ensured by encrypting sensitive data before outsourcing it to edge servers. The proposed lattice-based public key searchable encryption scheme in this paper achieves post-quantum security and highly flexible access control policies for multi-user applications.
As a bridge between cloud computing platforms and the Internet of Things (IoT) devices, edge computing provides various on-demand data services to reduce latency and network congestion. To ensure data security in edge computing, sensitive data should be encrypted before being outsourced to edge servers. Public key encryption with keyword search (PEKS) can provide search service for encrypted data. Nevertheless, most existing PEKS schemes are susceptible to quantum attacks because their security assumptions are based on traditional hardness assumptions. Moreover, many lattice based PEKS schemes only apply to the single-user scenario, limiting the range of applications. In this paper, we present an efficient lattice-based public key searchable encryption with fine-grained access control for edge computing. Our scheme achieves post-quantum security and highly flexible access control policies for multi-user applications, by utilizing the learning with errors (LWE) assumption and subset predicate encryption. The security proof illustrates that our proposed scheme is secure under chosen plaintext attacks and chosen keyword attacks. Finally, extensive experiments on real-world datasets illustrate that our encryption algorithm is faster than existing approaches. (C) 2021 Elsevier B.V. All rights reserved.

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