29. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving a shared binary anomaly detection model; comparing the shared binary anomaly detection model with a local anomaly detection model; combining the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting the input dataset based on whether the input dataset contains an anomaly.
29. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving a shared binary anomaly detection model; comparing the shared binary anomaly detection model with a local anomaly detection model; combining the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting the input dataset based on whether the input dataset contains an anomaly. 30. The medium of claim 29 , wherein the shared binary anomaly detection model is represented using a Bloom filter.