Privacy regulations, such as GDPR, ban the usage of information relatable to an identifiable person. Therefore, one can anonymize or de-identify the data before utilization.
Existing de-identification techniques often compromise data utility for privacy; they simply detect and delete personal information, taking away all other valuable information in the process.
Then, what is it that makes Anonymizer so different from other de-identifying technologies?
Anonymizer allows companies or ML developers to collect data that are usable for their target uses but also guarantee privacy. It is the only possible way to achieve both data utility and privacy regulation compliance.
While removing Personally Identifiable Information(PII), Anonymizer preserves data quality which is equivalent to the original. As data are anonymized, they become invisible to human but visible to AI, allowing users to train actual ML models while ensuring other's privacy.
The big change that only Anonymizer can bring is to develop machine learning models without using original data.
Anonymizer obfuscates data task-specifically for users. For instance, a data consumer who wants to build a cat detecting ML model is provided with anonymized data without any private information but with key attributes necessary for the cat detection.
With anonymized data provided by Deeping Source, users can train a new ML model(G) whose output is nearly identical to that of the original data.
Trained with anonymized data, model G is highly useful in actual environments where new original data are collected - that is to say, if anonymized with our Anonymizer, users can develop actual ML models even with anonymized data.
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