关键词: 隐私保护, 数据发布, 个性化差分隐私, 敏感程度评分, 属性关联

Abstract: Differential privacy is a widely adopted privacypreserving technique for data publication. However, existing methods typically apply uniform noise to the entire dataset, neglecting the fact that the sensitivity levels of different attributes in various datasets can vary significantly. This uniform approach often leads to unreasonable privacy budget allocation and diminished data utility. To address this issue, this paper proposes a data publication method based on multilayer sensitivity analysis for personalized differential privacy(MLSAPDP). The proposed method first designs a sensitivity scoring strategy that provides finegrained, comprehensive evaluation from the perspectives of individual attributes, tuples, and their interrelationships. Then, privacy budgets are personalized according to sensitivity levels. In addition, data clustering is used to group similar data, reducing global sensitivity and minimizing noise injection. This not only enhances privacy protection but also ensures high data utility. Experimental results demonstrate that compared to traditional differential privacy methods, the proposed approach more effectively protects sensitive data, achieving an optimized balance between privacy protection strength and data utility.

Key words: privacy protection, data publication, personalized differential privacy, sensitivity scoring, attribute correlation