As a common way to identify anomalous data, outlier detection is widely applicable for intrusions detection, adverse reactions analysis, financial fraud prevention, etc. The accuracy of outlier detection depends crucially on the number of data involved in the test, i.e., the more data participate in detection, the higher accuracy we get. For this reason, cross-dataset collaborative outlier detection is introduced to conquer the lack of data in a single-dataset setting. However, privacy concerns seriously prevent the application of collaborative outlier detection, since most organization are unwilling to share their data with others directly in practice. In this paper, we present efficient protocols for privacy preserving collaborative outlier detection from arbitrarily partitioned data using Local Distance-based Outlier Factor (LDOF). Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who detect outlier on the joint data by secure two-party computation. In particular, we perform arithmetic operations which takes place inside LDOF on arithmetic circuits instead of boolean circuits, and perform sorting operations on boolean circuits. Such a design enables standard operations are performed with suitable circuits, and thus our scheme is more efficient. In addition, to further improve protocol efficiency, local sensitive hash (LSH) is utilized to filter out data which do not need secure computation to reduce the the amount of shared data. We implement our system in C++ on real data. The security analysis and experiments show the security and efficiency of the proposed scheme. Our protocols are more faster than the state of previous methods.

作为识别异常数据的一种常用方法，离群值检测广泛用于入侵检测，不良反应分析，财务欺诈预防等。离群值检测的准确性主要取决于测试中涉及的数据数量，即，更多数据参与检测，我们可以获得更高的准确性。因此，引入了跨数据集协作离群值检测来克服单数据集设置中的数据不足。但是，由于大多数组织在实践中不愿直接与他人共享数据，因此隐私问题严​​重阻止了协作异常值检测的应用。在本文中，我们提出了一种有效的协议，用于使用基于局部距离的离群因子（LDOF）从任意划分的数据中保护隐私保护协作离群值检测。我们的协议属于两服务器模型，其中数据所有者在两个非竞争服务器之间分配其私有数据，这些服务器通过安全的两方计算来检测联合数据上的异常值。特别是，我们在算术电路而不是布尔电路上执行在LDOF内部进行的算术运算，并对布尔电路执行排序操作。这样的设计使标准操作可以在适当的电路上执行，因此我们的方案更加有效。此外，为了进一步提高协议效率，利用本地敏感哈希（LSH）过滤掉不需要安全计算即可减少共享数据量的数据。我们使用C ++在真实数据上实现我们的系统。通过安全性分析和实验证明了该方案的安全性和有效性。