As the amount of data generated by scaled systems in Electronic Medical Records (EMRs) and the Industrial Internet of Things (IIoT) keeps increasing, third-party servers are essential for data storage and manipulation, and with them come privacy concerns. Encrypting the uploaded data strips the server's ability to search over it for keywords: a highly desirable requirement in some use-cases as EMRs and IIoT. Subsequent efforts at constructing efficient and secure post-quantum searchable encryption schemes have failed to prevent a curious server from launching inside offline keyword guessing attack. For every intended receiver, the data owner performs computation separately, implying the requirement of prior knowledge about recipients (which is not practical in a use-case such as EMR) and a high overhead is incurred in the big data era. We provide a detailed cryptanalysis of existing theoretically secure schemes and leverage blockchain for load balancing. We then propose a scheme secure from an honest-but-curious server. We also present a detailed comparative analysis with existing schemes as well as efficient methods to mitigate blockchain overheads.
|Journal||Computers and Electrical Engineering|
|State||Published - Dec 2021|
Bibliographical noteFunding Information:
We thank the anonymous reviewers for their valuable comments which helped us to improve the quality, presentation, and organization of this paper.
© 2021 Elsevier Ltd
- Big data
- Decision Learning with Errors (LWE)
- Forward secrecy
- Provable security
- Searchable encryption
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science (all)
- Electrical and Electronic Engineering