IP Library Granted Patent US 12,204,514
Granted Patent B2
US 12,204,514 · App. 18/228,796 · Granted Jan 21, 2025

Ascribing a confidence factor for identifying a given column in a structured dataset belonging to a particular sensitive type

Inventors: Vilayannur Ramachandran Sitaraman (Milwaukee, WI); Subramanian Ramesh (Milwaukee, WI); Anhad Preet Singh (Milwaukee, WI)
Assignee: Dataguise, Inc.
G06F16/221G06F16/2282G06F16/2458G06F16/9017
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Quick Facts
Patent No.
US 12,204,514
App. No.
18/228,796
Granted
Jan 21, 2025
Kind
B2
Abstract

Various aspects of the subject technology relate to systems, methods, and machine-readable media for determining a confidence factor for a sensitive type. The method includes applying a set of matching procedures to cells in a structured data set, the structured data set comprising columns and/or rows. The method also includes counting hit counts for the cells, the hit counts corresponding to successful matches. The method also includes counting null counts for the cells, the null counts corresponding to cells having null or invalid values. The method also includes counting mishit counts for the cells, the mishit counts corresponding to cells that are not null and do not result in a match. The method also includes calculating the confidence factor based on the hit counts, the null counts, and the mishit counts, the confidence factor providing an effective probability that cells in the structured data set is of the sensitive type.

Claims (26)

1. A method for automated evaluation and encryption of a column of data in a structured data set, said method including:

receiving, at a computer, a structured electronic data set including at least one column of data cells, wherein said at least one column of data cells includes a header;

receiving, at said computer, a set of electronic identifiers of at least one sensitive data type;

comparing said set of electronic identifiers to said data cells of said at least one column of data cells;

electronically determining a hit count for said at least one column of data cells wherein said hit count corresponds to the number of data cells matching said at least one sensitive data type;

electronically determining a null count for said at least one column of data cells wherein said null count corresponds to the number of data cells having null or invalid values;

electronically determining a mishit count for said at least one column of data cells wherein said mishit count corresponds to the number of data cells that are not null and do not match said at least one sensitive data type;

electronically determining a confidence factor based on the hit count, the null count, and the mishit count, the wherein said confidence factor is based on the probability that any data cell in said structured data set matches said at least one sensitive data type;

when said column header matches said at least one sensitive data type, retrieving a predetermined, stored header weight from a memory;

electronically determining a weighted confidence factor based on said confidence factor and said header weight;

retrieving a predetermined, stored weighted confidence factor threshold from said memory; and

encrypting said at least one column of data cells when said weighted confidence factor is at least equal to said weighted confidence factor threshold.

2. A computerized system for automated evaluation and encryption of a column of data in a structured data set, said system including:

a memory storing a predetermined, stored header weight and a predetermined, stored weighted confidence factor threshold; and

a computerized data analysis system operating on a computer, wherein said computerized data analysis system receives:

a structured electronic data set including at least one column of data cells, wherein said at least one column of data cells includes a header; and

a set of electronic identifiers of at least one sensitive data type,

wherein said computerized data analysis system compares said set of electronic identifiers to said data cells of said at least one column of data cells and

electronically determines a hit count for said at least one column of data cells wherein said hit count corresponds to the number of data cells matching said at least one sensitive data type;

electronically determines a null count for said at least one column of data cells wherein said null count corresponds to the number of data cells having null or invalid values;

electronically determines a mishit count for said at least one column of data cells wherein said mishit count corresponds to the number of data cells that are not null and do not match said at least one sensitive data type;

electronically determines a confidence factor based on the hit count, the null count, and the mishit count, wherein said confidence factor is based on the probability that any data cell in said structured data set matches said at least one sensitive data type;

wherein, when said column header matches said at least one sensitive data type, said computerized data analysis system retrieves said predetermined, stored header weight from said memory and

electronically determines a weighted confidence factor based on said confidence factor and said header weight;

retrieves said predetermined, stored weighted confidence factor threshold from said memory; and

encrypts said at least one column of data cells when said weighted confidence factor is at least equal to said weighted confidence factor threshold.

Assignments (2)
SECURITY INTEREST Recorded Jun 2, 2025
From: DATAGUISE, INC.
To: AUDAX PRIVATE DEBT LLC, AS AGENT
Reel/Frame 071279/0520 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 5, 2023
From: SITARAMAN, VILAYANNUR RAMACHANDRAN; RAMESH, SUBRAMANIAN; SINGH, ANHAD PREET
To: DATAGUISE, INC.
Reel/Frame 064792/0436 →
Continuity (3)
Continuation 17676374 · Feb 21, 2022
Continuation 16823069 · Mar 18, 2020
Related Publication 20240028574A1 · Jan 25, 2024
References Cited (6)
US 6859791B1 · Spagna · 2005 [cited by examiner]
US 10528556B1 · Chmil · 2020 [cited by examiner]
US 20030110130A1 · Pelletier · 2003 [cited by examiner]
US 20110055192A1 · Tang · 2011 [cited by examiner]
US 20170293469A1 · Attaluri · 2017 [cited by examiner]
Hinton, G. E. (2002) Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, retrieved a copy on Jun. 17, 2020 from https://www.cs.toronto.edu/˜hinto/absps/tr00-004.pdf; http://gatsby.ucl.… [cited by applicant]