公开(公告)号：US20240037123A1

公开(公告)日：2024-02-01

申请号：US18484018

申请日：2023-10-10

申请人： PRIVACY ANALYTICS INC.

发明人： Muhammad Oneeb Rehman Mian ,  David Nicholas Maurice Di Valentino ,  George Wesley Bradley

IPC分类号： G06F16/28 ,  G06N5/04 ,  G06F16/21 ,  G06F18/213 ,  G06F18/21

CPC分类号： G06F16/285 ,  G06F16/288 ,  G06N5/04 ,  G06F16/211 ,  G06F18/213 ,  G06F18/217

摘要： Disclosed is a method for an intermediary mapping an de-identification comprising steps of retrieving datasets and meta data from a data source; selecting a target standard; mapping the retrieved datasets and the metadata to the target standard, wherein the datasets and the metadata are mapped to the target standard using one of, a schema mapping, a variable mapping, or a combination thereof; infer one or more of, variable classifications, variable connections, groupings, disclosure risk settings, and de-identification settings using the dataset mapping and metadata; perform a de-identification propagation using the mapped datasets, the mapped metadata, the inferred variable classifications, the inferred variable connections, the inferred groupings, the inferred disclosure risk settings, the inferred de-identification settings, or a combination thereof.

公开(公告)号：US20210049282A1

公开(公告)日：2021-02-18

申请号：US16991199

申请日：2020-08-12

申请人： Privacy Analytics Inc.

发明人： David Nicholas Maurice Di Valentino ,  Muhammad Oneeb Rehman Mian

IPC分类号： G06F21/57 ,  G06F21/62

摘要： Computing devices utilizing computer-readable media implement methods arranged for deriving risk contribution models from a dataset. Rather than inspect the entire data model in order to identify all quasi-identifying fields, the computing device develops a list of commonly-occurring but difficult-to-detect quasi-identifying fields. For each such field, the computing device creates a distribution of values/information values from other sources. Then, when risk measurement is performed, random simulated values (or information values) are selected for these fields. Quasi-identifying values are then selected for each field with multiplicity equal to the associated randomly-selected count. These are incorporated into the overall risk measurement and utilized in an anonymization process. In typical implementations, the overall average of re-identification risk measurement results prove to be generally consistent with the results which are obtained on the fully-classified data model.

公开(公告)号：US11782956B2

公开(公告)日：2023-10-10

申请号：US17505863

申请日：2021-10-20

申请人： PRIVACY ANALYTICS INC.

发明人： Muhammad Oneeb Rehman Mian ,  David Nicholas Maurice Di Valentino ,  George Wesley Bradley

IPC分类号： G06F7/00 ,  G06F16/28 ,  G06N5/04 ,  G06F16/21 ,  G06F18/213 ,  G06F18/21

CPC分类号： G06F16/285 ,  G06F16/211 ,  G06F16/288 ,  G06F18/213 ,  G06F18/217 ,  G06N5/04

摘要： Disclosed is a method for an intermediary mapping an de-identification comprising steps of retrieving datasets and meta data from a data source; selecting a target standard; mapping the retrieved datasets and the metadata to the target standard, wherein the datasets and the metadata are mapped to the target standard using one of, a schema mapping, a variable mapping, or a combination thereof; infer one or more of, variable classifications, variable connections, groupings, disclosure risk settings, and de-identification settings using the dataset mapping and metadata; perform a de-identification propagation using the mapped datasets, the mapped metadata, the inferred variable classifications, the inferred variable connections, the inferred groupings, the inferred disclosure risk settings, the inferred de-identification settings, or a combination thereof.

公开(公告)号：US20220129485A1

公开(公告)日：2022-04-28

申请号：US17505863

申请日：2021-10-20

申请人： PRIVACY ANALYTICS INC.

发明人： Muhammad Oneeb Rehman Mian ,  David Nicholas Maurice Di Valentino ,  George Wesley Bradley

IPC分类号： G06F16/28 ,  G06F16/21 ,  G06K9/62 ,  G06N5/04

摘要： Disclosed is a method for an intermediary mapping an de-identification comprising steps of retrieving datasets and meta data from a data source; selecting a target standard; mapping the retrieved datasets and the metadata to the target standard, wherein the datasets and the metadata are mapped to the target standard using one of, a schema mapping, a variable mapping, or a combination thereof; infer one or more of, variable classifications, variable connections, groupings, disclosure risk settings, and de-identification settings using the dataset mapping and metadata; perform a de-identification propagation using the mapped datasets, the mapped metadata, the inferred variable classifications, the inferred variable connections, the inferred groupings, the inferred disclosure risk settings, the inferred de-identification settings, or a combination thereof.