Can Synthetic Data Guarantee Better Safety?: 2 Limitations and 2 Strategies
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There is a risk of privacy leakage when using real datasets for AI learning. To prevent this, a method has emerged to create and use synthetic data instead of real data. Synthetic data does not directly expose sensitive information in real datasets, so it is actively used in many fields containing sensitive personal information, such as medicine and finance.
But is the use of synthetic data really safe? Let’s find out together.
Safety of Synthetic Data: Limitations
1. Possibility of re-identification
Synthetic data is created by mimicking the statistical patterns of real datasets. So, if synthetic data mimics the characteristics of real data too well, it is possible for a malicious user to re-identify the real data.
2. Indirect information exposure
If synthetic data reflects relationships within a real dataset, personal information may be exposed through indirect means. For example, synthetic medical records for a specific disease may indirectly expose information associated with real patients.
So, what should we do? To increase the safety of synthetic data, you can use the following strategies:
Strategies for safe use of synthetic data
1. Application of privacy protection technology
By applying privacy protection technologies such as Differential Privacy to generate synthetic data, the possibility of re-identification and indirect information exposure can be effectively reduced.
2. Introduction of verification process
Insecure synthetic data can be filtered out by introducing steps to verify the quality and privacy level of the data before using it.
In other words, using synthetic data generated in a simple way is risky. Select and use synthetic data whose safety has been verified, generated by applying the above-mentioned strategies so that you can utilize the data without ethical or legal issues:)
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