An Improved Fine-Grained Encryption Method for Unstructured Big Data

Zhou, Changli; Ma, Chunguang; Yang, Songtao

doi:10.1007/978-3-662-46248-5_44

Cited by 1 publication

(1 citation statement)

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“…The researcher in [14] performed an empirical investigation focused on utilizing big data for intelligent security analysis methods aimed at averting cyber threats. These methods are designed to safeguard information assets by assessing and predicting risks.…”

Section: Introductionmentioning

confidence: 99%

Elevating Big Data Privacy: Innovative Strategies and Challenges in Data Abundance

Elkawkagy,

Elwan,

Alsumayt

et al. 2024

IEEE Access

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The exponential growth of big data has ushered in transformative possibilities across various sectors, but it has also raised formidable privacy concerns. This article delves into the pressing need for enhancing big data privacy and explores innovative approaches to address this critical issue. In recent years, big data has been characterized by its immense volume, high velocity, and diverse data sources. These attributes have enabled organizations to gain unprecedented insights but have also exposed sensitive information to potential breaches. As such, ensuring the privacy of individuals and sensitive data within big data sets has emerged as a paramount concern. This article first elucidates the multifaceted nature of big data privacy, emphasizing its encompassment of privacy, confidentiality, integrity, and availability. It also acknowledges the challenges posed by existing privacy-preserving techniques, which often fall short of providing comprehensive protection for large and diverse data sets. The core focus of this article lies in presenting novel strategies and technologies designed to improve big data privacy. This article presents an innovative framework that combines advanced encryption methods, including fine-grained encryption techniques and differential privacy mechanisms specifically designed for the distinct traits of big data, like noisy techniques. To achieve this, the dataset undergoes categorization into key attributes, sensitive attributes, quasi attributes, and insensitive attributes. Subsequently, the fine-grained technique encrypts key and sensitive attributes, while the differential privacy mechanism encrypts the quasi attributes. To further substantiate the effectiveness of the proposed technique, this article references to empirical findings that demonstrate tangible improvements in big data privacy protection.

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Section: Introductionmentioning

confidence: 99%