18066767. DATASET PRIVACY MANAGEMENT SYSTEM simplified abstract (Accenture Global Solutions Limited)
Contents
- 1 DATASET PRIVACY MANAGEMENT SYSTEM
DATASET PRIVACY MANAGEMENT SYSTEM
Organization Name
Accenture Global Solutions Limited
Inventor(s)
Bini Samuel Yao of Cagnes-Sur-Mer (FR)
DATASET PRIVACY MANAGEMENT SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18066767 titled 'DATASET PRIVACY MANAGEMENT SYSTEM
Simplified Explanation
The dataset evaluation system described in the abstract processes a target dataset to identify quasi-identifiers and compute an inference risk score based on the presence of these quasi-identifiers.
- The system receives a target dataset and generates a normalized version of it.
- It then compares the normalized target dataset with an intruder dataset to identify any quasi-identifiers present.
- A Cartesian product of the two datasets is determined, and an inference risk score is computed using a distance linkage disclosure technique.
- The system outputs information associated with the inference risk score.
Potential Applications
This technology could be applied in data security and privacy compliance tools, as well as in data breach prevention systems.
Problems Solved
This technology helps in identifying potential privacy risks in datasets, especially when shared or used in conjunction with other datasets.
Benefits
The system provides a quantitative measure of the inference risk associated with the target dataset, allowing for better decision-making in data sharing and processing.
Potential Commercial Applications
"Data Inference Risk Assessment System for Privacy Compliance Tools"
Possible Prior Art
One possible prior art could be the use of distance linkage disclosure techniques in data privacy and security assessments.
Unanswered Questions
How does this technology handle large-scale datasets?
The abstract does not specify how the system scales when processing large volumes of data. It would be important to understand the system's performance in such scenarios.
What are the computational requirements of this system?
The abstract does not mention the computational resources needed to run the dataset evaluation system. Understanding the system's resource demands would be crucial for implementation.
Original Abstract Submitted
In some implementations, a dataset evaluation system may receive a target dataset. The dataset evaluation system may-processing the target dataset to generate a normalized target dataset. The dataset evaluation system may process the normalized target dataset with an intruder dataset to identify whether any quasi-identifiers are present in the normalized target dataset. The dataset evaluation system may determine a Cartesian product of the normalized target dataset and the intruder dataset. The dataset evaluation system may compute, using a distance linkage disclosure technique, an inference risk score for the target dataset with the intruder dataset based on the Cartesian product and whether any quasi-identifiers are present in the normalized target dataset. The dataset evaluation system may output information associated with the inference risk score.
