Invention Grant
US09576152B2 Data privacy employing a k-anonymity model with probabalistic match self-scoring
有权
数据隐私采用具有概率匹配自我评分的k匿名模型
- Patent Title: Data privacy employing a k-anonymity model with probabalistic match self-scoring
- Patent Title (中): 数据隐私采用具有概率匹配自我评分的k匿名模型
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Application No.: US14680516Application Date: 2015-04-07
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Publication No.: US09576152B2Publication Date: 2017-02-21
- Inventor: Lawrence Dubov , Scott Schumacher
- 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 Terry J. Carroll
- Main IPC: G06F21/62
- IPC: G06F21/62 ; G06F17/30 ; G06N7/00 ; H04L29/06
Abstract:
According to one embodiment of the present invention, a system for protecting data determines a desired duplication rate based on a level of desired anonymity for the data and generates a threshold for data records within the data based on the desired duplication rate. The system produces a data record score for each data record based on comparisons of attributes for that data record, compares the data record scores to the threshold, and controls access to the data records based on the comparison. Embodiments of the present invention further include a method and computer program product for protecting data in substantially the same manners described above.
Public/Granted literature
- US20160034715A1 DATA PRIVACY EMPLOYING A K-ANONYMITY MODEL WITH PROBABALISTIC MATCH SELF-SCORING Public/Granted day:2016-02-04
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