Abstract Monitoring web browsing behavior can benefit for many data mining tasks, such as top-k mining and suspicious behavior watching. However, directly releasing private browsing data to the public would raise user concerns from a privacy perspective. Differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in correlated data. For this reason, Pufferfish privacy, a recent generalization of differential privacy for correlated data, can be used. The goal of our work is to share useful statistics of on-line browsing behavior to perform monitoring tasks while protecting individual user privacy. To achieve this goal, the privacy requirements in our problem are specified in the Pufferfish framework firstly. Then a privacy leakage computation model (PLCM) is designed based on the previous privacy specification, which can be used to make a quantitative analysis of the maximum privacy leakage caused by temporal correlations. Since the computational complexity of PLCM is too high and cannot meet the real-time requirement, three strategies (bounding the number of secret pairs, limiting the maximum length of sessions and avoiding solving the subproblems repeatedly) are proposed to promote efficiency thirdly. At last, a privately continual release algorithm for web monitoring is presented based on the maximum privacy leakage calculated in the previous steps, which can reduce the computational complexity and the added noise significantly. Formal privacy analysis shows that our scheme satisfies ϵ-Pufferfish privacy. Extensive experiment results on the real-world dataset illustrate that our scheme outperforms other state-of-the-art techniques.

中文翻译：

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一种在时间相关性下监控网页浏览行为的河豚隐私机制

摘要 监控网页浏览行为可以有益于许多数据挖掘任务，例如 top-k 挖掘和可疑行为观察。但是，直接向公众发布隐私浏览数据会从隐私角度引起用户的担忧。差分隐私是当前数据隐私的黄金标准，但并未充分解决相关数据中的隐私问题。出于这个原因，可以使用河豚隐私，这是对相关数据的差分隐私的最新概括。我们工作的目标是共享有用的在线浏览行为统计数据，以执行监控任务，同时保护个人用户隐私。为了实现这一目标，我们问题中的隐私要求首先在 Pufferfish 框架中指定。然后基于之前的隐私规范设计隐私泄漏计算模型（PLCM），该模型可用于对时间相关性引起的最大隐私泄漏进行定量分析。由于PLCM的计算复杂度太高，不能满足实时性要求，提出了三种策略（限制秘密对的数量，限制会话的最大长度和避免重复求解子问题）来提高效率。最后，基于前面步骤中计算的最大隐私泄漏，提出了一种用于网络监控的隐私持续发布算法，该算法可以显着降低计算复杂度和增加的噪声。正式的隐私分析表明，我们的方案满足 ϵ-Pufferfish 隐私。