Abstract: An approach is provided for domain-specific chatbots that employ distributed natural language classifiers (NLCs). A NLC included in a first chatbot is executed, which generates a first intent of a question and a first confidence that the first intent is an actual intent of the question. Based on a determination that the first confidence does not exceed a threshold, the question is broadcasted to other chatbots. A second confidence that a second intent is an actual intent is received from a second chatbot in response to the broadcast, and is determined to exceed the threshold. An association among the question, the second chatbot, the second confidence, and the second intent is stored in a memory cache included in the first chatbot. A response to the question is generated based on the second intent. The response is presented to a user from the first chatbot.

Abstract: An approach is provided for domain-specific chatbots that employ distributed natural language classifiers (NLCs). A NLC included in a first chatbot is executed, which generates a first intent of a question and a first confidence that the first intent is an actual intent of the question. Based on a determination that the first confidence does not exceed a threshold, the question is broadcasted to other chatbots. A second confidence that a second intent is an actual intent is received from a second chatbot in response to the broadcast, and is determined to exceed the threshold. An association among the question, the second chatbot, the second confidence, and the second intent is stored in a memory cache included in the first chatbot. A response to the question is generated based on the second intent. The response is presented to a user from the first chatbot.

Abstract: A method and a system for preserving privacy of a dataset are provided. In the method, a k-anonymity value with respect to a sensitive data field is determined according to at least one first quasi-identifier. Data entries in each group have the same value in the one or more fields of the first quasi-identifier and data entries in different groups have different values in the one or more fields of the first quasi-identifier. A first group and a second group among the plurality of groups are determined according to the reference number Kr, where the first group and the second group are merged into a merging group. The number of data entries in the merging group is not less than a reference number Kr. One or more fields of at least one first quasi-identifier is masked for the merging group.

Abstract: A method and a system for preserving privacy of a dataset are provided. In the method, a k-anonymity value with respect to a sensitive data field is determined according to at least one first quasi-identifier. Data entries in each group have the same value in the one or more fields of the first quasi-identifier and data entries in different groups have different values in the one or more fields of the first quasi-identifier. A first group and a second group among the plurality of groups are determined according to the reference number Kr, where the first group and the second group are merged into a merging group. The number of data entries in the merging group is not less than a reference number Kr. One or more fields of at least one first quasi-identifier is masked for the merging group.