.png){kind=icon}
.png){kind=icon}
Return
Toward Secure Data Computation and Outsource for Multi-User Cloud-Based IoT
.png){kind=icon}
PRE
AI
DOI：10.1109/TCC.2021.3087614.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cloud computing has promoted the success of Internet of Things (IoT) with offering abundant storage and computation resources where the data from IoT sensors can be remotely outsourced to the cloud servers, whereas storing, exchanging and processing data collected through IoT sensors via centralised or decentralised cloud servers make cloud-based IoT systems prone to internal or external attacks. To protect IoT data against potential malicious users and adversaries, some cryptographic schemes have been applied to ensure confidentiality and integrity of IoT data. It is however a challenging task to perform any arithmetical computations once data items are encrypted. Fully-homomorphic encryption which is based on lattices can, in principle, provide a solution, but it is unfortunately inefficient in computation and hence cannot be applied to IoT. Fully-homomorphic encryption is feasible when we allow the involvement of a semi-trusted server. However, it is challenging to provide such a system in the situation of distributed environments for shared IoT data. We solve this problem and provide a fully-homomorphic encryption scheme for cloud-based IoT applications. We introduce a new method with the aid of a semi-trusted server that can help compute the homomorphic multiplications without gaining any useful information of the encrypted data. We show how our scheme is applied to multi-user IoT security and prove its semantic security. We also conduct experiments to justify its efficiency and applicability to multi-user cloud-based IoT systems.
Keywords:
IoT
cloud computing
homomorphic encryption
data security
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.8K
Citations:
4.3K
Organization
View more organizations