Y. Xiong, M. X. Luo
Searchable Encryption Scheme for Large Data Sets in Cloud Storage Environment
Číslo: 2/2024
Periodikum: Radioengineering Journal
DOI: 10.13164/re.2024.0223
Klíčová slova: Cloud computing, searchable encryption, data sets, security
Pro získání musíte mít účet v Citace PRO.
Anotace:
Cloud storage has become essential in managing and retrieving extensive volumes of data, providing economical alternatives and adaptability for effective storage environment. However, in light of the rapid expansion of comprehensive datasets in cloud storage, the preservation of security has emerged as a matter of utmost importance for large data sets. Encryption has become a crucial mechanism for protecting confidential large data sets from unauthorized individuals. Encryption is necessary for safeguarding sensitive data by transforming it into indecipherable code so prevent unauthorized entry, and the encryption and decryption process is done at the end-user and cloud server. In the present situation, searchable symmetric encryption assumes a pivotal function by facilitating safe data retrieval while concurrently upholding the principle of secrecy. This research presents the Searchable Encryption Scheme in Cloud Storage Environment (SES-CSE), which offers a resilient solution for tackling the obstacles related to data security and retrieval efficiency for large data sets. The SES-CSE framework effectively incorporates encryption techniques inside a robust search engine, establishing a reliable framework for large data sets protection with Okapi BM25. The approach exhibits significant performance benefits, as shown by an encryption time of 14.85 ms, decryption time of 10.06 ms, memory consumption of 77.87 MB, and search times of 13.5 ms. The SES-CSE model demonstrates remarkable retrieval accuracies of 98.41%, 98.57%, and 97.51% throughout the training, testing, and validation phases. The results underscore the usefulness and security of SES-CSE as a solution for cloud storage, improving both the secrecy of data and the efficiency of retrieval in large-scale settings. This paper is part of special issue AI-DRIVEN SECURE COMMUNICATION IN MASSIVE IOT FOR 5G AND BEYOND.