TripleBlind at Work: Brokering Genetic Data

Genetic data is quickly becoming one of the most useful tools we have as human beings for understanding how our bodies work and how to better manage our health on an individual, highly-specialized level. Genetic data also tells us about our history, who our ancestors were, and is connecting dots for people in ways they never could have previously imagined.

While genetic data is being collected at a much more rapid pace than ever before, it is still difficult to use. The future of healthcare very well might involve a high degree of reliance on genetic analysis and use of that data to develop personalized medicines and treatments. But, in this case, the benefits are also the risks. Having highly specific information tied to an individual is an incredibly powerful tool for progressing the healthcare industry, but it also imposes privacy and misuse risks. 

Many privacy laws and regulations, including HIPAA, require that data be anonymized or otherwise de-identified before it can be shared or used. In the case of genetic information, because an individual’s genetic sequence is unique to them, there is no way to sufficiently de-identify that information. Even if you strip away the name, date of birth, sex, and other identifiers, the data itself can be traced back to an individual within a reasonable degree of certainty. This extreme sensitivity of its contents has made genetic data a challenge to work with. 

Luckily, there is a solution. TripleBlind’s technology can automatically de-identify genomic data in real time to accelerate AI and collaboration in healthcare. In fact, we’re working with BC Platforms to enable their partners to logically (not physically) aggregate sensitive genomic sequence data into large, usable datasets, allowing them to accomplish their mission of advancing personalized medicine by connecting healthcare and research.

Using Blind Learning, TripleBlind’s novel method for training and running inferences on AI models using disparate sources without moving either the data or the full model, healthcare enterprises can collaborate using sensitive data that has historically been unusable due to data privacy regulations, competitive pressures, and operational complexities. TripleBlind tools offer drug development companies the ability to collaborate with multiple data providers at once by supporting big data analysis, while ensuring private data can never be decrypted or misused and enforcing the appropriate privacy regulations on top of each interaction – including HIPAA, GDPR, CCPA, and more.

 

Pharmaceutical companies using TripleBlind’s Private Data Sharing Solution will be able to develop better drugs with accessibility to a larger pool of de-identified genetic data. Additionally, as a result of TripleBlind’s one-way data encryption and Blind De-identification, all parties reduce liability and risk of data breaches during collaboration.

Ultimately, TripleBlind’s ability to facilitate the private sharing of genetic data will unlock doors for healthcare entities that will allow for faster and more accurate diagnosis, better drug development and more accurate patient testing; a fact that was also shown using EKG Data in collaboration with Mayo Clinic.

To learn more about how TripleBlind’s technology can be applied in real-world situations for industries including healthcare and financial services, connect with us on Twitter and LinkedIn to stay updated on our Use Case Blog Series. Contact us at contact@tripleblind.ai to schedule a free demo.