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Monitoring Web Browsing Behavior with Differential Privacy

机译：使用差异隐私监视Web浏览行为

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Monitoring web browsing behavior has benefited many data mining applications, such as top-K discovery and anomaly detection. However, releasing private user data to the greater public would concern web users about their privacy, especially after the incident of AOL search log release where anonymization was not correctly done. In this paper, we adopt differential privacy, a strong, provable privacy definition, and show that differentially private aggregates of web browsing activities can be released in real-time while preserving the utility of shared data. Our proposed algorithms utilize the rich correlation of the time series of aggregated data and adopt a state-space approach to estimate the underlying, true aggregates from the perturbed values by the differential privacy mechanism. We evaluate our algorithms with real-world web browsing data. Utility evaluations with three metrics demonstrate that the quality of the private, released data by our solutions closely resembles that of the original, unperturbed aggregates.
机译：监视Web浏览行为已使许多数据挖掘应用程序受益，例如top-K发现和异常检测。但是，向更广泛的公众发布私人用户数据将使Web用户担心他们的隐私，尤其是在AOL搜索日志发布事件中，匿名处理不正确之后。在本文中，我们采用了差异性隐私，这是一个强有力的，可证明的隐私性定义，并表明可以在不影响共享数据实用性的情况下实时发布网络浏览活动的差异性私有集合。我们提出的算法利用聚集数据的时间序列的丰富相关性，并采用状态空间方法通过差分隐私机制从扰动值中估计潜在的真实聚集。我们使用真实的Web浏览数据评估我们的算法。具有三个指标的效用评估表明，我们的解决方案发布的私有数据的质量与原始不受干扰的聚合的质量非常相似。

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