Big Data Based on T-Closeness Processing Breaks the Information Cocoon

Abstract

Aiming at the phenomenon of information cocoon and the challenge of privacy protection in the era of big data, this paper deeply discusses the role of t-closeness and differential privacy technology in breaking the information cocoon, enhancing data privacy protection and promoting information diversity. As an optimized data anonymization technology, t-closeness prevents privacy leakage, reduces algorithm bias and information homogeneity by limiting the distribution of sensitive attributes in equivalence classes, and provides an effective way to break information cocooning. This paper reviews the current situation of t-closeness technology at home and abroad, especially the fuzzy t-closeness method proposed by Chen Xiaoyu et al., which reduces information loss and improves the intensity of privacy protection through fuzzy processing. As another important privacy protection technology, differential privacy can ensure the privacy of individual data by adding random noise to data query, especially in the aspect of high-dimensional data processing. The LDP algorithm based on union tree proposed by Cheng Siyuan et al., effectively improves the processing efficiency and data quality.