The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 15, 2020

Filed:

Aug. 01, 2018
Applicant:

The Regents of the University of Michigan, Ann Arbor, MI (US);

Inventors:

Ivaylo Dinov, Ann Arbor, MI (US);

John Vandervest, Ann Arbor, MI (US);

Simeone Marino, Ann Arbor, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 21/62 (2013.01); G16H 10/60 (2018.01); G06N 7/08 (2006.01); G06F 17/18 (2006.01); G06F 17/11 (2006.01); G16H 50/70 (2018.01); G06F 16/22 (2019.01); G06N 5/00 (2006.01); G06N 7/00 (2006.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06F 21/6254 (2013.01); G06F 16/2272 (2019.01); G06F 17/11 (2013.01); G06F 17/18 (2013.01); G06N 7/08 (2013.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G06N 5/003 (2013.01); G06N 7/005 (2013.01); G06N 20/20 (2019.01);
Abstract

A method is presented for generating a data set from a database. The method involves iterative data manipulation that stochastically identifies candidate entries from the cases (subjects, participants) and variables (data elements) and subsequently selects, nullifies, and imputes the information. This process heavily relies on statistical multivariate imputation to preserve the joint distributions of the complex structured data archive. At each step, the algorithm generates a complete dataset that in aggregate closely resembles the intrinsic characteristics of the original data set, however, on an individual level the rows of data are substantially altered. This procedure drastically reduces the risk for subject reidentification by stratification, as meta-data for all subjects is repeatedly and lossily encoded.


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