Amazon technologies, inc. (20240256704). EFFICIENT STATISTICAL TECHNIQUES FOR DETECTING SENSITIVE DATA simplified abstract
Contents
EFFICIENT STATISTICAL TECHNIQUES FOR DETECTING SENSITIVE DATA
Organization Name
Inventor(s)
Aurelian Tutuianu of Iasi (RO)
Petru-Serban Cehan of Iasi (RO)
Silviu Catalin Poede of Iasi (RO)
Marian-Razvan Udrea of Iasi (RO)
EFFICIENT STATISTICAL TECHNIQUES FOR DETECTING SENSITIVE DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256704 titled 'EFFICIENT STATISTICAL TECHNIQUES FOR DETECTING SENSITIVE DATA
- Simplified Explanation:**
A method is proposed to identify attribute combinations in a dataset that are similar to sensitive information data types, generate input features for a machine learning model, and predict the presence of sensitive information in the dataset.
- Key Features and Innovation:**
- Identification of attribute combinations meeting data type similarity criteria.
- Generation of input features for machine learning model.
- Prediction of presence of sensitive information using the model.
- Potential Applications:**
This technology can be applied in data security, privacy protection, and compliance with data regulations.
- Problems Solved:**
Addresses the challenge of identifying and protecting sensitive information in datasets.
- Benefits:**
Enhances data security, aids in compliance with regulations, and improves privacy protection.
- Commercial Applications:**
Potential commercial applications include data security software, privacy protection tools, and compliance solutions for businesses handling sensitive information.
- Prior Art:**
Researchers can explore prior work in data privacy, machine learning for data analysis, and data security to understand the background of this technology.
- Frequently Updated Research:**
Stay updated on advancements in data privacy regulations, machine learning algorithms for data analysis, and data security measures to enhance the application of this technology.
- Questions about Attribute Combination Identification:**
1. How does the method identify attribute combinations similar to sensitive data types? 2. What are the implications of using machine learning models to predict the presence of sensitive information in datasets?
By providing answers to these questions, readers can gain a deeper understanding of the technology and its applications in data security and privacy protection.
Original Abstract Submitted
a candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. a collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. an indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.
