Machine learning and rule-based identification, anonymization, and de-anonymization of sensitive structured and unstructured data
US12045373B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 17, 2021 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | Oct 21, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In some examples, machine learning and rule-based identification, anonymization, and de-anonymization of sensitive structured and unstructured data may include receiving input data that is to be masked, and determining, for the input data, at least one type '1 of entity extraction from a plurality of types of entity extractions to be performed on the input data. The at least one determined type of entity extraction may be performed on the input data, and at least one entity may be extracted from the input data. At least one replacement strategy may be determined from a plurality of replacement strategies for the at least one extracted entity. Further, the at least one determined replacement strategy may be applied to the at least one extracted entity to generate masked data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.