对抗性信息融合侵犯企业数据隐私
On Breaching Enterprise Data Privacy Through Adversarial Information Fusion
摘要：
数据隐私是当今企业面临的主要挑战之一。匿名化技术通过净化敏感数据来解决这个问题，使得个人隐私得到保护，同时允许企业维护和共享敏感数据。然而，关于这个问题的现有工作对数据进行了固有的假设，这些假设在日常企业数据管理场景中是不切实际的。此外，对企业数据应用现有的匿名化方案可能导致对抗攻击，其中入侵者可能使用信息融合技术来造成隐私泄露。在本文中，我们阐明了当前匿名化方案在企业数据背景下的缺点。我们定义并实验证明了基于Web的信息融合攻击匿名企业数据。我们制定了融合弹性企业数据分析的问题，并提出了一个原型解决方案来解决这个问题。
英文摘要：
Data privacy is one of the key challenges faced by enterprises today. Anonymization techniques address this problem by sanitizing sensitive data such that individual privacy is preserved while allowing enterprises to maintain and share sensitive data. However, existing work on this problem make inherent assumptions about the data that are impractical in day-to-day enterprise data management scenarios. Further, application of existing anonymization schemes on enterprise data could lead to adversarial attacks in which an intruder could use information fusion techniques to inflict a privacy breach. In this paper, we shed light on the shortcomings of current anonymization schemes in the context of enterprise data. We define and experimentally demonstrate Web-based Information- Fusion Attack on anonymized enterprise data. We formulate the problem of Fusion Resilient Enterprise Data Anonymization and propose a prototype solution to address this problem.
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