View source for 18066767. DATASET PRIVACY MANAGEMENT SYSTEM simplified abstract (Accenture Global Solutions Limited)

=DATASET PRIVACY MANAGEMENT SYSTEM=


==Organization Name==

[[:Category:Accenture Global Solutions Limited|Accenture Global Solutions Limited]]


[[Category:Accenture Global Solutions Limited]]

==Inventor(s)==

[[:Category:Baya Dhouib of Valbonne (FR)|Baya Dhouib of Valbonne (FR)]][[Category:Baya Dhouib of Valbonne (FR)]]

[[:Category:Bini Samuel Yao of Cagnes-Sur-Mer (FR)|Bini Samuel Yao of Cagnes-Sur-Mer (FR)]][[Category: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.



[[Category:G06F21/62]]
[[Category:G06F18/15]]
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