Synthetic Data Generation for Privacy-Safe Testing
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Solution Overview
Problem
The challenge lies in providing application developers with user data for testing purposes while ensuring user privacy and adhering to legal standards, as direct access to actual user data poses security risks and legal complexities, especially when dealing with sensitive information like biometric or health data.
Innovation Solution
An electronic device and method that utilize a generative adversarial network (GAN) to create data similar to original user data, allowing for the provision of processed data that mimics user data, thereby addressing security and legal concerns by generating pseudo data that can be used for testing without exposing actual sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If actual user data is provided to developers for testing, then testing effectiveness is improved, but user privacy security deteriorates
Solution Approach 1:
The patent creates synthetic test data that copies the statistical properties and structural characteristics of actual user data without containing real sensitive information. The synthetic data is generated through a process that replicates data distributions, relationships, and patterns while ensuring no actual user information is exposed to developers during testing.
Solution Approach 2:
The patent introduces an intermediary data processing layer that transforms actual user data into synthetic test data through a controlled generation process. This intermediary layer acts as a buffer between the actual sensitive data and the developers, allowing testing to proceed without direct access to real user information while maintaining data utility.
2Productivity
If actual user data is accessed by developers, then data availability for testing is improved, but legal compliance deteriorates
Solution Approach 1:
The patent generates synthetic data that legally replicates the statistical characteristics and structural properties of actual user data. This synthetic copy maintains data availability for testing purposes while eliminating legal compliance issues associated with accessing and sharing actual sensitive user information across organizational boundaries.
Solution Approach 2:
The patent creates an intermediary data generation process that produces test data through a controlled transformation of actual data. This intermediary mechanism ensures that developers receive data sufficient for testing while the transformation process maintains legal compliance by preventing direct access to and transfer of actual sensitive information.
3Object-affected harmful factors
If processed data similar to user data is generated, then privacy protection is improved, but data similarity to original data deteriorates
Solution Approach 1:
The patent transforms actual user data into synthetic data by changing specific parameters and properties while preserving the overall statistical distribution and structural characteristics. The transformation modifies individual data points and relationships to eliminate sensitive information while maintaining the data's utility and similarity for effective testing.
Data Source
AI summary
According to an embodiment, an electronic device comprises at least one processor, and a memory that stores instructions configured to cause the at least one processor to obtain first data associated with original data based on random number using a first program, obtain first similarity information between the original data and the first data, obtain second data associated with the original data based on the random number using a second program, obtain second similarity information between the original data and the second data, in response to receiving a request, and provide the first program or the second program based on information included in a request that corresponds to a range that includes at least one of the first similarity information or the second similarity information.