Abstract: Data publishing is an important way to promote data sharing. However, studies have shown that it is accompanied by the risk of privacy leakage of sensitive attribute information due to large and frequent access to data by malicious parties. Although existing evaluation methods consider the scenarios of above issue, most of them are noninteractive and static settings. Therefore, in practical applications, dynamic risk cannot be accurately assessed by data publishers, and even the noise protection causes an imbalance between privacy and utility. In this paper, we propose an interactive dynamic privacy risk assessment and adaptive protection method. It firstly utilizes machine learning prediction models to determine the privacy level of difficulttodefine attributes. And secondly we sets a mechanism to dynamically adjust the privacy level according to the actual requests of the data demander and timely restricts the leakage of sensitive attribute caused by highfrequency access. Finally, an adaptive noiseadding mechanism is proposed after obtaining the high risk of evaluation feedback, to ensure that the balance of privacy and utility achieveing good results when the data is released. Experimental results show that the privacy risk index increases gradually with the number of accesses and the accesses containing associated attributes lead to an increase in the privacy risk index by more than 15%. Adaptive noise addition, on the other hand, provides a similar level of privacy protection to overall noise addition at 0.5, yet improves data utility by more than 30% over overall noise addition.

Key words: data publishing; interactive dynamics; privacy risk assessment; privacy-utility tradeoff; adaptive noise addition

摘要： 数据发布是推动数据共享的重要方式.然而研究显示，数据发布中伴随着恶意需求方大量频繁访问获取数据导致敏感属性信息的隐私泄露风险.现有的评估方法虽然考虑了数据发布与需求双方的场景，但大多是非交互式的静态设定.因此在实际应用中不能准确评估数据发布方面临的持续变化的隐私风险，且加噪保护多造成隐私与效用不平衡.为此，提出了一种数据发布场景下交互式动态隐私风险评估及保护方法.该方法首先运用机器学习预测模型确定难以界定的属性的隐私等级；其次设定动态调整隐私等级机制，根据数据需求方的实际请求及时调整隐私等级并及时限制高频访问导致的敏感属性信息泄露；最后在获取评估反馈风险较高后对数据进行隐私保护时提出了适应性加噪机制，确保在数据发布时隐私风险效用平衡达到良好效果.实验结果显示，该方法隐私风险指数会随访问数量增加而逐步提高，且包含关联属性的访问导致隐私风险指数提高15%以上；而适应性加噪在噪声达到0.5时与整体加噪隐私保护水平相近，却比整体加噪对数据效用的提升超过30%.

关键词: 数据发布；交互式动态；隐私风险评估；隐私效用平衡；适应性加噪