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

摘要： 差分隐私是一种广泛用于数据去隐私后发布的隐私保护主流技术，但现有方法通常对整个数据集进行统一加噪处理.实际场景中不同数据集中各属性值的敏感程度不同，统一加噪会导致隐私预算分配不合理，降低数据可用性.针对上述问题，提出了一种基于多层敏感分析的个性化差分隐私数据发布方法(multilayer sensitivity analysis for personalized differential privacy, MLSAPDP).首先设计了一种敏感程度评分策略，从属性和元组自身及其关联关系角度实现细粒度综合评估.其次根据敏感程度个性化分配隐私预算，并通过数据聚类分组降低全局敏感度，减少噪声注入，在提升隐私保护的同时保证数据效用.实验结果表明，相比传统差分隐私方法，该方法能更有效地保护敏感数据，实现了隐私保护强度与数据效用之间的优化平衡.

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