Journal
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
Volume 111, Issue -, Pages 152-161Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jpdc.2017.08.007
Keywords
Internet of Things; Cloud-of-Things; Searchable encryption; Forward privacy; File-injection attack resilience; Insider keyword guessing attack resilience
Categories
Funding
- National Natural Science Foundation of China [61472287, 61501333, 61572379, 61402339]
- National High-tech R&D Program of China (863 Program) [2015AA016004]
- open fund of Guangxi Key Laboratory of Cryptography and Information Security [GCIS201608]
- Natural Science Foundation of Hubei Province of China [2015CFA068, 2015CFB257]
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Internet of things (IoT) applications comprising thousands or millions of intelligent devices or things is fast becoming a norm in our inter-connected world, and the significant amount of data generated from IoT applications is often stored in the cloud. However, searching encrypted data (i.e. Searchable Encryption-SE) in the cloud remains an ongoing challenge. Existing SE protocols include searchable symmetric encryption (SSE) and public-key encryption with keyword search (PEKS). Limitations of SSE include complex and expensive key management and distribution, while PEKS suffer from inefficiency and are vulnerable to insider keyword guessing attacks (KGA). Besides, most protocols are insecure against file-injection attacks carried out by a malicious server. Thus, in this paper, we propose an efficient and secure searchable encryption protocol using the trapdoor permutation function (TPF). The protocol is designed for cloud-based IoT (also referred to as Cloud of Things - CoT) deployment, such as Cloud of Battlefield Things and Cloud of Military Things. Compared with other existing SE protocols, our proposed SE protocol incurs lower computation cost at the expense of a slightly higher storage cost (which is less of an issue, considering the decreasing costs of storage). We also prove that our protocol achieves inside KGA resilience, forward privacy, and file-injection attack resilience. (C) 2017 Elsevier Inc. All rights reserved.
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