Privacy-Preserving Social Media Data Outsourcing
Privacy-Preserving Social Media Data Outsourcing
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DOI：
10.1109/infocom.2018.8486242
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Jinxue Zhang;Jingchao Sun;Rui Zhang;Yanchao Zhang;Xia Hu
中科院分区：
文献类型：
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作者：
Jinxue Zhang;Jingchao Sun;Rui Zhang;Yanchao Zhang;Xia Hu
用户生成的社交媒体数据呈爆炸式增长，在公共和私营部门都有很高的需求。完整社交媒体数据的泄露加剧了对用户隐私的威胁。在本文中，我们首先识别了当前数据外包实践中基于文本的用户链接攻击，其中可以根据用户未受保护的文本数据确定匿名数据集中的真实用户。然后，我们在文献中首次提出了一个差异化隐私保护社交媒体数据外包的框架。在我们的框架内，社交媒体数据服务提供商可以外包受干扰的数据集，为用户提供不同的隐私，同时为社交媒体数据消费者提供高数据效用。我们的差分隐私机制基于电子文本不可区分性的新概念，我们提出该机制来阻止基于文本的用户链接攻击。在真实世界和合成数据集上进行的大量实验证实，我们的框架可以实现高水平的差分隐私保护和高数据实用性。
User-generated social media data are exploding and of high demand in public and private sectors. The disclosure of intact social media data exacerbates the threats to user privacy. In this paper, we first identify a text-based user-linkage attack on current data outsourcing practices, in which the real users in an anonymized dataset can be pinpointed based on the users' unprotected text data. Then we propose a framework for differentially privacy-preserving social media data outsourcing for the first time in literature. Within our framework, social media data service providers can outsource perturbed datasets to provide users differential privacy while offering high data utility to social media data consumers. Our differential privacy mechanism is based on a novel notion of E - text indistinguishability, which we propose to thwart the text-based user-linkage attack. Extensive experiments on real-world and synthetic datasets confirm that our framework can enable high-level differential privacy protection and also high data utility.