Hospitals with highly advanced diagnostics algorithms can broaden their impact through licensing or otherwise allowing third party use of their AI algorithms without fear of reverse engineering. TripleBlind makes this possible so that remote healthcare diagnostics centers can perform faster diagnoses on real patient data.
TripleBlind enables diagnostic algorithms to operate on one-way encrypted and de-identified data. Any data, including X-ray images and EKGs, can be used at its highest resolution without incurring an accuracy penalty. The solution provides many advantages over the five methods for data anonymization most frequently utilized today. Blind de-identification does not alter the fidelity of the data, while:
- 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 the general properties of the original data.
TripleBlind allows operations on data in real-time without needing to generate an anonymized basket of data that is a snapshot of the past. The path from data collection to data usage is significantly faster, cheaper and seamless using Blind De-identification. Fewer data preparation steps translate to lower data project costs, less legal paperwork and more powerful insights that use the complete, unaltered dataset in the most private way currently possible.
In addition to offering the best privacy for data in-use, our solution importantly and revolutionarily protects the intellectual property of algorithms, with a breakthrough one-way algorithm encryption capability which protects algorithms-in-use from common attacks aimed at reverse engineering an algorithm for reconstructing the data that went into training an AI model.
Now, the world’s best diagnostic algorithms can be put to use for remote diagnostics, without exposing valuable IP, while preserving HIPAA compliance via TripleBlind’s Private Data Sharing Solution.
Read more from our Use Case blog series:
TripleBlind at Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data
TripleBlind at Work: Mayo Clinic
TripleBlind at Work: Alternative Data
TripleBlind at Work: Early Indication Trial Reporting