17932938. SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES simplified abstract (Oracle International Corporation)
Contents
- 1 SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES
Organization Name
Oracle International Corporation
Inventor(s)
Rajan Madhavan of Foster City CA (US)
Madalasa Venkataraman of Bengaluru (IN)
Girish Nautiya of Bengaluru (IN)
Dinesh Ghanta of Bangalore (IN)
SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17932938 titled 'SYSTEMS FOR DESIGN AND IMPLEMENTATION OF PRIVACY PRESERVING AI WITH PRIVACY REGULATIONS WITHIN INTELLIGENCE PIPELINES
Simplified Explanation
The abstract of the patent application describes a method for protecting privacy in data sets by adding noise to personally identifiable information (PII) attributes before training a machine-learning model using the protected data.
- Identification of privacy protection protocols for data
- Categorization of PII attributes based on data field format
- Addition of noise to processed PII data
- Training a machine-learning model using the protected data
Potential Applications
The technology described in the patent application could be applied in various industries such as healthcare, finance, and marketing where sensitive data needs to be protected while still allowing for analysis and modeling.
Problems Solved
1. Protecting the privacy of users' data 2. Ensuring compliance with data protection regulations
Benefits
1. Enhanced data security and privacy 2. Improved accuracy of machine-learning models by training on protected data
Potential Commercial Applications
Protecting customer data in e-commerce platforms
Possible Prior Art
One possible prior art could be differential privacy techniques used in data analysis to protect individual privacy.
Unanswered Questions
How does the noise addition affect the accuracy of the machine-learning model?
The abstract does not provide details on how the amount of noise added to the data impacts the performance of the machine-learning model.
Are there any limitations to the categorization of PII attributes based on data field format?
The abstract does not mention any potential challenges or limitations in categorizing PII attributes in this manner.
Original Abstract Submitted
Data can be received that includes information corresponding to a set of users. Privacy protection protocols that apply to the data can be identified. A subset of the data can be identified as being personally identifiable information (PII) data, where the subset includes a set of PII attributes. The PII attributes can be split into categories based on a format of a data field in the PII attributes. The processed PII data can be combined with non-PII data to create processed client data. It can be determined to add noise to part of the processed PII data. An amount of noise can be determined based on the privacy protection protocols. The amount of noise can be added to part of the processed PII data to produce protected data. A machine-learning model can be trained using the protected data.
