TripleBlind is currently the only solution that effectively de-identifies genomic data. Its groundbreaking approach to data sharing involves de-identification via one-way encryption that allows for safe and compliant data sharing among healthcare institutions. The solution meets the legal definition of de-identification, and TripleBlind never hosts any data that is being shared.
TripleBlind unlocks the ability for healthcare organizations to share PHI, health records, genomic and other data, enabling data to be usable at its highest resolution without incurring an accuracy penalty. TripleBlind de-identifies data by splitting each record, randomly, byte-by-byte, automatically de-identifying it without anonymizing it. Because the random splits cannot be used to identify an individual, the data sharing remains compliant with privacy standards, like HIPAA and GDPR.
Blind de-identification via one way encryption provides many advantages over the five methods for data anonymization most frequently utilized today, the utmost being that blind de-identification does not alter the fidelity of the data. Apart from often being slow, expensive, and unclear as to if full sets of data are actually fully de-identified and secure, other methods of de-identification remain inferior to TripleBlind’s mode of blind de-identification.
- K-anonymization alters the fidelity of the data through two means: suppression (data masking); certain values of the attributes are replaced by an asterisk. All or some values of a column may be replaced by an asterisk; or generalization; individual values of attributes are replaced with a broader category, e.g., the value 19 might be replaced with <20,
- Pseudonymization replaces private identifiers with fake identifiers or pseudonyms,
- Data swapping (shuffling or permutation) rearranges the dataset attribute values so they do not correspond with the original records,
- Data perturbation modifies the original data set by rounding numbers and adding random noise, also known as differential privacy,
- Synthetic data is often used in place of altering the original dataset or using it as is and risking privacy, but even the best synthetic data is still a replica of specific properties of the original data.
One way encryption creates a clear path from data collection to data usage that is significantly faster, cheaper, seamless and compliant.
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