How TripleBlind’s Data Privacy Solution Compares to Synthetic Data
Synthetic data is a representative set of data that looks and feels like the real thing, but does not contain real customer or patient information. It is commonly used to enable collaboration when generic stand-ins for real datasets are acceptable for the use case, though some loss of fidelity is unavoidable for any application of synthetic data generation.
For example, synthetic data may be used by a credit card aggregator to determine macro trends from the underlying customer data. This application is acceptable because it is difficult and costly for the aggregator to obtain data from banks and credit card providers. In this situation, synthetic data is sufficient to glean industry trends from the data.
Another problem with sharing synthetic data is that outlying data is often omitted, making the dataset inaccurate. This also makes the dataset vulnerable to spear-phishing or cross-correlation.
TripleBlind is far superior to sharing synthetic data because businesses can fully analyze real data in order to understand real trends. TripleBlind’s solution allows for data collaboration without jeopardizing privacy or compliance. Data shared through TripleBlind’s solution remains de-identified, private and can only be used for its intended purpose.
As shown in the above chart, collaboration via synthetic data has a negligible impact in most categories where accuracy and compliance are necessary. On the contrary, TripleBlind’s solution fulfills criteria across the board, making it a superior way to share data.
To learn more about how TripleBlind compares to other competitors and methods of data collaborations, follow us on LinkedIn and Twitter to be notified when we post the next installation in our Competitor Blog Series.
If you’d like to schedule a call or free demo to explore how TripleBlind can work for your business, please reach out to contact@tripleblind.com.
Read the other blogs in this series:
Business Agreements
Homomorphic Encryption
Blockchain
Tokenization, Masking and Hashing
Federated Learning
Differential Privacy