Abstract: A data market system located at the Internet includes a storage circuit, an input surface circuit, and a processor. The input surface circuit is used for receiving a query and an encrypted data packet. The processor is used for controlling the input surface circuit to receive the encrypted data packet and to store the encrypted data packet in the storage circuit. The processor processes the encrypted data packet according to the query. The encrypted data packet corresponds to at least one of anonymization application programming interface, blockchain, differential privacy, and homomorphic encryption provided by the data market system.

Abstract: A data collection and analysis method includes applying a first noise step to an original data stream with an original character to generate a first data stream with a first character; and applying a second noise step to the first data stream to generate a second data stream with a second character, wherein a first variation between the original character and the first character is greater than a second variation between the original character and the second character.

Abstract: The present invention provides a method for monitoring tracks and a track monitoring module. The method comprises: maintaining a target list, stored a target track, by a monitoring server; recording a first track related to a first mobile device by a first monitoring application; and comparing the similarity between the first track and the target track by the first monitoring application to generate a comparison result.

Abstract: A data collection and analysis method includes applying a first noise step to an original data stream with an original character to generate a first data stream with a first character; and applying a second noise step to the first data stream to generate a second data stream with a second character, wherein a first variation between the original character and the first character is greater than a second variation between the original character and the second character.

Abstract: A data collection and analysis method includes applying a first noise step to an original data stream with an original character to generate a first data stream with a first character; and applying a second noise step to the first data stream to generate a second data stream with a second character, wherein a first variation between the original character and the first character is greater than a second variation between the original character and the second character.

Abstract: A de-identification data generation apparatus, method, and non-transitory computer readable storage medium thereof are provided. The apparatus is stored with a plurality of original records, wherein each of the records has a plurality of original values corresponding to a plurality of attributes one-to-one. The apparatus decides a plurality of attribute relations (including a user-defined attribute relation) according to the original values, wherein each attribute relation is defined by two attributes. The apparatus decides a plurality of relation groups according to the attribute relations. For each relation group, the apparatus calculates a statistical distribution of the original values corresponding to the attributes in the relation group, aggregates the statistical distribution into a plurality of sub-statistical distributions, and adds noise to each sub-statistical distribution individually.

Abstract: A malware detection apparatus, a malware detection method, and a computer program product thereof are provided. The malware detection apparatus is used to detect a program. The program executes a first process. The malware detection apparatus comprises a storage unit and a processing unit. The storage unit is configured to store a malicious behavior profile of a malware. The processing unit is configured to construct a first behavior profile according to the first process, compare the first behavior profile with the malicious behavior profile and generate a comparison result. The processing unit updates a behavior record table according to the comparison result, and determines that the program is the malware according to the behavior record table.