A Review of Data Privacy Protection Technologies in the Big Data Environment

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With the rapid development and widespread application of big data technologies, while personal data generates immense social value, the risk of privacy breaches is also escalating. How to effectively protect personal privacy while ensuring data usability has become a central issue of shared societal concern. Existing reviews often focus on describing individual privacy protection technologies, lacking a multidimensional, cross-comparative analysis of various technical approaches. This paper reviews mainstream privacy protection technologies in the big data environment, classifying them into three major categories: data distortion, data encryption, and data anonymization, while also introducing emerging solutions such as federated learning and trusted execution environments. Through a comparative analysis of various technologies across three dimensions—privacy protection strength, data utility, and computational efficiency—this paper finds that each technology has its own strengths and weaknesses in terms of privacy protection; currently, no single technology can simultaneously meet all requirements, and there is a fundamental core conflict between privacy protection and data utility. In practice, appropriate technologies should be selected based on specific business scenarios, or an implementation approach combining multiple technologies should be adopted.