公开(公告)号：GB2587942A

公开(公告)日：2021-04-14

申请号：GB202017391

申请日：2019-04-24

Applicant: IBM

Inventor： PATRICK WATSON ,  MARIA CHANG ,  TENGFEI MA ,  ALDIS SIPOLINS

IPC: G06N20/00

Abstract: Techniques that facilitate layered stochastics anonymization of data are provided. In one example, a system includes a machine learning component and an evaluation component. The machine learning component performs a machine learning process for first data associated with one or more features to generate second data indicative of one or more example datasets within a degree of similarity to the first data. The first data and the second data comprise a corresponding data format. The evaluation component evaluates the second data for a particular feature from the one or more features and generates third data indicative of a confidence score for the second data.

公开(公告)号：GB2608033A

公开(公告)日：2022-12-21

申请号：GB202212229

申请日：2021-01-18

Applicant: IBM

Inventor： ZHONGSHU GU ,  XIAOKUI SHU ,  HANI JAMJOOM ,  TENGFEI MA

IPC: G06F11/36 ,  G06F9/00 ,  G06N3/10

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.