Abstract: Methods and systems for handling of an inbound Stream Control Transmission Protocol (SCTP) packet at an SCTP load balancer (SCTP LB) are described. In one embodiment a method includes receiving, at the SCTP LB, an SCTP packet; determining, by the SCTP LB, which tunneling mechanism to use for the SCTP packet; and tunneling the SCTP packet to a correct back-end server for further processing of the SCTP packet.

Abstract: A prediction consensus system receives predicted event values for an event. The predicted event values are generated by various computer models that may have different training schemes and input data. The prediction consensus system generates a consensus value based on the predicted event values after excluding outliers. Outliers are determined among the predicted event values based on statistical measures of the predicted event values as well as observed event values. The predicted event values that appear to be outliers based on the predicted statistical measures may nonetheless be included when they are within the observed statistical measures. This ensures the predicted event values used in the consensus values are not unduly biased towards other predicted event values. Similarly, the identification of event values as accepted or an outlier may be returned as training data for retraining the computer models that generated the predicted event values.

Abstract: A schema matching system processes training event data received from multiple sources to determine correspondence rules associating fields in the schemas of each source. To generate the correspondence rules, the schema matching system can use training event data from multiple sources comprising events associated with multiple schemas. Then, based on one or more similarity metrics between data entries of the training event data, the system matches individual events using a machine learning algorithm and, based on the pairs of matching events, corresponding schema fields across the multiple schemas. Based on the matching events and/or user feedback, the schema matching system can generate one or more correspondence rules based on the normalization rules and the corresponding fields of the schemas for later use by one or more transaction monitoring systems on incoming event data.

Abstract: A schema matching system processes training event data received from multiple sources to determine correspondence rules associating fields in the schemas of each source. To generate the correspondence rules, the schema matching system can use training event data from multiple sources comprising events associated with multiple schemas. Then, based on one or more similarity metrics between data entries of the training event data, the system matches individual events using a machine learning algorithm and, based on the pairs of matching events, corresponding schema fields across the multiple schemas. Based on the matching events and/or user feedback, the schema matching system can generate one or more correspondence rules based on the normalization rules and the corresponding fields of the schemas for later use by one or more transaction monitoring systems on incoming event data.

Abstract: Systems, methods, and computer-executable instructions for secure data analysis using encrypted data. An encryption key and a decryption key are created. The security of encryption using the encryption key and the decryption key are based upon factoring. A computation key is created based upon the encryption key. Data is encrypted using the encryption key. The encrypted data and the computation key are provided to a remote system. The remote system is requested to perform data analysis on the encrypted data. An encrypted result of the data analysis is received from the remote system. The encrypted result of the data analysis is decrypted with the decryption key.

Abstract: Systems, methods, and computer-executable instructions for homomorphic data analysis. Encrypted data is received, from a remote system, that has been encrypted with an encryption key. A number of iterations to iterate over the encrypted data is determined. A model is iterated over by the number of iterations to create an intermediate model. Each iteration updates the model, and the model and the intermediate model encrypted with the encryption key. The intermediate model is provided to the remote system. An updated model based upon the intermediate model is received from the remote system. The updated model is iterated over until a predetermined precision is reached to create a final model. The final model is provided to the remote system. The final model is encrypted with the encryption key.

Abstract: Systems, methods, and computer-executable instructions for homomorphic data analysis. Encrypted data is received, from a remote system, that has been encrypted with an encryption key. A number of iterations to iterate over the encrypted data is determined. A model is iterated over by the number of iterations to create an intermediate model. Each iteration updates the model, and the model and the intermediate model encrypted with the encryption key. The intermediate model is provided to the remote system. An updated model based upon the intermediate model is received from the remote system. The updated model is iterated over until a predetermined precision is reached to create a final model. The final model is provided to the remote system. The final model is encrypted with the encryption key.

Abstract: Systems, methods, and computer-executable instructions for secure data analysis using encrypted data. An encryption key and a decryption key are created. The security of encryption using the encryption key and the decryption key are based upon factoring. A computation key is created based upon the encryption key. Data is encrypted using the encryption key. The encrypted data and the computation key are provided to a remote system. The remote system is requested to perform data analysis on the encrypted data. An encrypted result of the data analysis is received from the remote system. The encrypted result of the data analysis is decrypted with the decryption key.