To achieve robust global anomaly detection models, different companies or organizations should share their
knowledge of data. However, the sharing of production data will lead to violation of privacy. It is unaccepted to co-operate
with the risk of disclose private or sensitive data. The existing distributed anomaly detection techniques always neglect
the requirement and are based on the sharing or exchanging of production data. The proposed privacy preserving distributed
anomaly detection method employs local model sharing technology to preserve the privacy of data. Mean while, the proposed
method has comparable or even better performance on the synthetic as well as several real life data sets by seven different
anomaly detection models.