A Hybrid Approach for Enhancing Data Aggregation Security in the Internet of Medical Things
Abhishek Bajpai, Akash Maurya, Anita Yadav · 2023
In this study, we focus on addressing the security and privacy challenges associated with the Internet of Medical Things (IoMT) and associated data. IoMT involves the transmission of large volumes of real-time medical data from various small devices. To tackle these challenges, we propose a novel approach that combines the Lightweight Encryption Algorithm (LEA) with the Paillier encryption approach. This combination is aimed at improving energy consumption, encryption time, and memory usage. By employing LEA encryption, our approach ensures both confidentiality and data integrity during the transmission of medical data. Additionally, the Paillier encryption technique is leveraged to preserve privacy and facilitate secure data aggregation. Through extensive security analysis and experimental evaluations, we have found that our proposed method achieves average encryption and decryption speeds that are 30% faster and 8% less energy consumption compared to the ECC-AES and ElGamal algorithms.