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Author:

Zhang, Hanlin (Zhang, Hanlin.) | Yu, Jia (Yu, Jia.) | Tian, Chengliang (Tian, Chengliang.) | Tong, Le (Tong, Le.) | Lin, Jie (Lin, Jie.) | Ge, Linqiang (Ge, Linqiang.) | Wang, Huaqun (Wang, Huaqun.)

Indexed by:

EI SCIE Scopus Engineering Village

Abstract:

Rivest-Shamir-Adleman (RSA) is one of the widely deployed public-key algorithms. Yet, its decryption facet is very time consuming for resource-constrained Internet-of-Thing (IoT) devices, as it is based on the modular exponentiation of a large number. Although several variants of RSA have been designed to accelerate decryption, the outcomes have been far from satisfactory. Therefore, it is of imminent importance to investigate how to securely outsource RSA decryption to computational powerful parties as an alternative solution. In this article, we introduce the first efficient and secure outsourcing scheme for RSA decryption in IoT. Though RSA decryption is achieved via modular exponentiation, existing secure outsourcing schemes for modular exponentiation either assume the modulus to be prime and are not applicable to RSA or incur massive computation costs and are heavy laden in practice. To address these issues, we have designed our scheme based on the Chinese remainder theorem (CRT). In our scheme, the private keys (including the exponent and the modulus) and the plaintext are concealed concurrently, and the proposed scheme is highly efficient for both client and cloud. In addition, our scheme enables the client to detect any misbehavior of the cloud server with a probability of 99.17%. To validate the effectiveness of our proposed scheme, we provide rigorous proofs of security and verifiability, as well as efficiency analysis. The effectiveness and efficiency of our scheme are further confirmed based on experimental results. © 2014 IEEE.

Keyword:

Computation theory Cryptography Efficiency Internet of things Outsourcing

Author Community:

  • [ 1 ] [Zhang, Hanlin]College of Computer Science and Technology and the Business School, Qingdao University, Qingdao; 266071, China
  • [ 2 ] [Yu, Jia]College of Computer Science and Technology, Qingdao University, Qingdao; 266071, China
  • [ 3 ] [Tian, Chengliang]College of Computer Science and Technology, Qingdao University, Qingdao; 266071, China
  • [ 4 ] [Tong, Le]College of Computer Science and Technology, Qingdao University, Qingdao; 266071, China
  • [ 5 ] [Lin, Jie]School of Electronic and Information Engineering, Xi'An Jiaotong University, Xi'an; 710049, China
  • [ 6 ] [Ge, Linqiang]Department of Computer Science, Georgia Southwestern State University, Americus; GA; 31709, United States
  • [ 7 ] [Wang, Huaqun]Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing; 210023, China

Reprint Author's Address:

  • [Yu, Jia]College of Computer Science and Technology, Qingdao University, Qingdao; 266071, China;;

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Source :

IEEE Internet of Things Journal

Year: 2020

Issue: 8

Volume: 7

Page: 6868-6881

9 . 4 7 1

JCR@2020

9 . 4 7 1

JCR@2020

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count: 16

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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