Synthetic Data Generation System for Privacy Protection
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Solution Overview
Problem
Existing communication network data usage poses significant privacy and security concerns due to unsupervised monitoring, despite the valuable insights that real data can provide for applications like marketing and customer service.
Innovation Solution
A system for generating fabricated pattern data records (XDRs) using software agents and a core module that models and generates synthetic data from real context-related data sources, ensuring privacy and security through data splitting, storage, and transformation, while utilizing algorithms for pattern creation and synthetic data production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real communication network data is used for analysis, then valuable insights for marketing and customer service can be obtained, but user privacy and security are compromised due to unsupervised monitoring
Solution Approach 1:
The patent creates synthetic copies of real communication data that preserve the statistical patterns and relationships needed for analysis while completely removing personally identifiable information. The synthetic data generation module produces artificial call detail records that mimic the structure and distribution of real data without containing actual user information, thus enabling valuable analytics while protecting user privacy and security
Solution Approach 2:
The patent introduces synthetic data as an intermediary between real data and analysis applications. Instead of directly using sensitive real user data, the system generates synthetic data that serves as a safe mediator, allowing analysts to obtain insights without direct exposure to real personal information, thus resolving the contradiction between data utility and privacy protection
2Object-affected harmful factors
If synthetic data is generated to protect privacy, then user security is maintained, but data similarity and analytical accuracy may be reduced
Solution Approach 1:
The patent carefully adjusts parameters of the synthetic data generation process to optimize the balance between privacy protection and data similarity. The system modifies parameters such as call duration distributions, frequency patterns, and network topology characteristics to ensure synthetic data maintains sufficient fidelity for accurate analysis while preserving privacy, allowing tunable control over the similarity-privacy tradeoff
Data Source
Figure 1
AI summary
A system for generating fabricated pattern data records (XDRs) based on data from accessible data sources, which comprises an XDR core module containing one or more modeling and pattern creation modules for modeling original data received from the data sources; one or more synthetic data generation modules for generating fabricated data, based on the patterns created by the modeling and pattern creation modules; a data splitting module for splitting the data into training and testing sets according to a predetermined policy; an XDR storage database for storing created patterns and fabricated data; a configuration manager for controlling the operation of the modeling and pattern creation modules and of the synthetic data generation modules; a plurality of XDR agents being software components for communicating with the data sources and accessing relevant data, using a unique API of each data source. Each of the XDR agents is capable of identifying the data-structures of its corresponding data source; transforming the data structures into a unified input structure being used by the XDR core module; a data-store communication module for mediating between the XDR agents and the XDR core modules by using data transformation.