Post-Quantum Secure Identity-Based Encryption Scheme using Random Integer Lattices for IoT-enabled AI Applications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on th...

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Bibliographic Details
Published in:Security and communication networks 2022-07, Vol.2022, p.1-14
Main Authors: Dharminder, Dharminder, Das, Ashok Kumar, Saha, Sourav, Bera, Basudeb, Vasilakos, Athanasios V.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Big Data
Computer networks
Computers
Cryptography
Data analysis
Deep learning
Electronic devices
Encryption
Integers
Internet of Things
Lattices
Machine learning
Nodes
Quantum computing
Satellite communications
Security
Sensors
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Summary:Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, where the IoT smart devices send securely the sensing data to their nearby gateway nodes(s) with the help of IBE and the gateway node(s) secure aggregate the data from the smart devices by decrypting the messages using the proposed IBE decryption. Later, the gateway nodes will securely send the aggregated data to the cloud server(s) and the Big data analytics is performed on the authenticated data using the Artificial Intelligence (AI)/Machine Learning (ML) algorithms for accurate and better predictions.
ISSN:1939-0114
1939-0122
DOI:10.1155/2022/5498058