Invention Grant
- Patent Title: Data de-identification across different data sources using a common data model
-
Application No.: US16447069Application Date: 2019-06-20
-
Publication No.: US10936752B2Publication Date: 2021-03-02
- Inventor: Aris Gkoulalas-Divanis
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Edell, Shapiro & Finnan, LLC
- Agent Will Stock
- Main IPC: G06F21/62
- IPC: G06F21/62 ; G06F21/57 ; G06F21/60 ; G06F16/21

Abstract:
A computer system migrates and de-identifies data. Data is migrated from a dataset to a common data model that is configured to accommodate data comprising a plurality of different data types to be de-identified. Data is analyzed in the common data model to identify privacy vulnerabilities and determine corresponding data de-identification techniques and configuration options to be applied to the data. The automatically determined data de-identification techniques are applied to the data to address all of the identified privacy vulnerabilities, and the resulting de-identified data is migrated from the common data model back to the dataset. Embodiments of the present invention further include a computer-implemented method and program product for migrating and de-identifying data in substantially the same manner described above.
Public/Granted literature
- US20190303619A1 DATA DE-IDENTIFICATION ACROSS DIFFERENT DATA SOURCES USING A COMMON DATA MODEL Public/Granted day:2019-10-03
Information query