Privacy-preserving anomaly detection in cloud with lightweight homomorphic encryption
作者：
Highlights：
• Cloud based models for anomaly detection poses critical challenges to data privacy.
• A cloud based privacy preserving anomaly detection model is proposed.
• The framework relies on lightweight homomorphic encryption to preserve data privacy.
• Data clustering based anomaly detection performed in a scalable manner on ciphertext.
• High detection accuracy is achieved with less complexity compared to other methods.
摘要
•Cloud based models for anomaly detection poses critical challenges to data privacy.•A cloud based privacy preserving anomaly detection model is proposed.•The framework relies on lightweight homomorphic encryption to preserve data privacy.•Data clustering based anomaly detection performed in a scalable manner on ciphertext.•High detection accuracy is achieved with less complexity compared to other methods.
论文关键词：Data privacy,Anomaly detection,Data clustering,Homomorphic Encryption,Cloud computing
论文评审过程：Received 21 December 2015, Revised 14 September 2016, Accepted 1 March 2017, Available online 20 March 2017, Version of Record 14 September 2017.
论文官网地址：https://doi.org/10.1016/j.jcss.2017.03.001
