Abstract: A data anonymity method and a data anonymity system are provided. The data anonymity method includes the following steps. A data set comprising a plurality of direct-identifiers, a plurality of quasi-identifiers and a plurality of event logs each of which includes an activity and a timestamp is obtained. A content of each of the direct-identifiers is replaced by a pseudonym. The quasi-identifiers are classified, via a group-by algorithm with k-anonymity, as a plurality of equivalence classes. The activities corresponding to each of the direct-identifiers are linked according to the timestamps to obtain a plurality of event sequences. A similarity hierarchy tree is obtained according to a plurality of edit distances among the event sequences. The event sequences are grouped according to the similarity hierarchy tree with k-anonymity to obtain at least one group. The event sequences which are in the group are generalized.

Abstract: A privacy data integration method and a server are provided. The privacy data integration method includes the following steps. A first processing device and a second processing device respectively obtain a first generative model and a second generative model according to a first privacy data and a second privacy data. A server generates a first generative data and a second generative data via the first generative model and the second generative model respectively. The server integrates the first generative data and the second generative data to obtain a synthetic data.

Abstract: A data anonymity method and a data anonymity system are provided. The data anonymity method includes the following steps. A data set comprising a plurality of direct-identifiers, a plurality of quasi-identifiers and a plurality of event logs each of which includes an activity and a timestamp is obtained. A content of each of the direct-identifiers is replaced by a pseudonym. The quasi-identifiers are classified, via a group-by algorithm with k-anonymity, as a plurality of equivalence classes. The activities corresponding to each of the direct-identifiers are linked according to the timestamps to obtain a plurality of event sequences. A similarity hierarchy tree is obtained according to a plurality of edit distances among the event sequences. The event sequences are grouped according to the similarity hierarchy tree with k-anonymity to obtain at least one group. The event sequences which are in the group are generalized.

Abstract: A privacy data integration method and a server are provided. The privacy data integration method includes the following steps. A first processing device and a second processing device respectively obtain a first generative model and a second generative model according to a first privacy data and a second privacy data. A server generates a first generative data and a second generative data via the first generative model and the second generative model respectively. The server integrates the first generative data and the second generative data to obtain a synthetic data.