A Framework of Privacy Preserving Anomaly Detection
Hiromi Arai, Keita Emura, Takuya Hayashi · 2017
Collecting and analyzing personal data is important in modern information applications. Though the privacy of data providers should be protected, some adversarial users may behave badly under circumstances where they are not identified. However, the privacy of honest users should not be infringed. Thus, detecting anomalies without revealing normal users-identities is quite important for operating information systems using personal data. Though various methods of statistics and machine learning have been developed for detecting anomalies, it is difficult to know in advance what anomaly will come up. Thus, it would be useful to provide a "general" framework that can employ any anomaly detection method regardless of the type of data and the nature of the abnormality.