Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption cryptography for privacy preserved data access in cloud assisted Internet of Things services using glow‐worm swarm optimization. Issue 12 (6th September 2022) - Suma, M. R.; Madhumathy, P.
Abstract: Health monitoring sensors are widely available to monitor patients' health remotely. The collected sensor data are sent to cloud storage and processed in a remote health monitoring system. The Internet of Things (IoT) with cloud support offers a promising answer to data exploding concerns for the ability to constrain specific gadgets. However, IoT faces many security issues when sharing data between two users because of the cloud's leverage nature. In the case of a private cloud, simple encryption techniques can ensure security. But, in the case of a public cloud, maintaining data privacy is a significant issue. To overcome this concern, an optimized Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption (BGV‐FHE) encryption method for secured data mutuality is proposed in this article. In this method, IoT medical data are initially acquired and then encrypted using two key schemes, and the data are stored in cloud storage. The main objective of these key schemes is to select optimal key parameters. Key parameters are chosen using the glow‐worm swarm optimization and used for the encryption phase. Storing data in the cloud using a public key provides access to requested users. Meanwhile, sensitive data are encrypted using a private key and cannot be accessed unless authenticated. This proposed approach has two levels of encryption, and thus it is an efficient data secured protocol for cloud resources. Implementing the optimized BGV‐FHE scheme based encryption scheme in Python, two datasets result in an accuracy level of 77% and 91%, respectively. Abstract : After storing the data in the cloud, it is accessible on request. As per the fully HE standard, data from the cloud is able to be computed directly without decrypting. Decryption was done using the decryption algorithm of the private key, and the public key is kept visible to all users, and data access using the public key was general data. General data includes registration details of patients, and sensitive data is left to be unlocked by the private key. The main phases of the implemented framework are data encryption, storage, and decryption. Data encryption is used to hide important data or information in the cloud with the goal of prohibiting unapproved access. This encryption process employs optimal key creation, which is aided by the use of a meta‐heuristic algorithm known as glow worm swarm optimization. By considering multi‐objective functions, optimal key generation is regularized. Again, sensitive data are decrypted using a key for accessing data. This proposed framework has hybridized optimization methods and encryption methods benefits of providing an efficient way of changing security parameters.