Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach
Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach
复制标题
保护隐私的政府数据发布：基于雾计算的差分隐私方法
DOI：
10.1016/j.future.2018.07.038
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发表时间：
2019-01-01
影响因子：
7.5
通讯作者：
Liu, Liping
中科院分区：
文献类型：
--
作者：
Piao, Chunhui;Shi, Yajuan;Liu, Liping
随着公共开放数据的日益增多，公民隐私权的保护已成为政府数据公开的一个重要问题。然而，当前政府云平台存在大量的运营风险。当云平台受到攻击时，如果攻击者具有先验背景知识，现有的数据发布隐私保护模型大多无法抵抗攻击。潜在的攻击者可能会获取已发布的统计数据，并识别特定个人的背景信息，这可能会导致公民隐私信息的泄露。为了解决这个问题，我们提出了一种基于雾计算的差分隐私保护的数据发布方法在本文中。讨论了政府统计数据发布中公民隐私泄露的风险，提出了一种基于雾计算的政府统计数据发布的差异隐私框架。在此基础上，设计了一种基于MaxDiff直方图的数据发布算法，实现了基于雾计算的用户隐私保护功能。利用差分方法对原始数据进行拉普拉斯噪声处理，即使攻击者有较强的背景知识，也能防止公民隐私泄露。根据最大频率差对相邻数据箱进行分组，构造出平均误差最小的差分隐私直方图。我们评估所提出的方法通过计算实验的基础上的真实的数据集的菲律宾家庭的收入和支出的Kaggle。实验结果表明，该数据发布方法不仅能有效保护公民隐私，而且降低了查询敏感度，提高了发布数据的实用性。(C)2018由Elsevier B.V.出版
With the growing availability of public open data, the protection of citizens' privacy has become a vital issue for governmental data publishing. However, there are a large number of operational risks in the current government cloud platforms. When the cloud platform is attacked, most existing privacy protection models for data publishing cannot resist the attacks if the attacker has prior background knowledge. Potential attackers may gain access to the published statistical data, and identify specific individual's background information, which may cause the disclosure of citizens' private information. To address this problem, we propose a fog-computing-based differential privacy approach for privacy-preserving data publishing in this paper. We discuss the risk of citizens' privacy disclosure related to governmental data publishing, and present a differential privacy framework for publishing governmental statistical data based on fog computing. Based on the framework, a data publishing algorithm using a MaxDiff histogram is developed, which can be used to realize the function of preserving user privacy based on fog computing. Applying the differential method, Laplace noises are added to the original data set, which prevents citizens' privacy from disclosure even if attackers get strong background knowledge. According to the maximum frequency difference, the adjacent data bins are grouped, then the differential privacy histogram with minimum average error can be constructed. We evaluate the proposed approach by computational experiments based on the real data set of Philippine families' income and expenditures provided by Kaggle. It shows that the proposed data publishing approach can not only effectively protect citizens' privacy, but also reduce the query sensitivity and improve the utility of the data published. (C) 2018 Published by Elsevier B.V.