TripleBlind Versus Competing Solutions

One of the most frequent questions we hear is, “How is TripleBlind different from other solutions?” Our technology is too detailed to explain in a short answer, especially when other technologies in this space are being developed by big names like Microsoft and IBM. However, it’s too important to go unaddressed. Over the course of several blogs, we will be going into detail about these other technologies such as homomorphic encryption, federated learning, blockchain, differential privacy, synthetic data, tokenization, and the old-fashioned – business agreement. 

Current solutions use one or some of these approaches, but TripleBlind is superior. We know that key providers such as Enveil, Baffle, Duality Technologies, Google, IBM, Intel, Microsoft, PreVeil offer homomorphic encryption and encryption-in-use camp. Some of the providers that leverage differential privacy include Immuta, Microsoft and SAP.

Providers that use synthetic data are Mostly.AI, Statice, Syntho and Tonic. For tokenization, IBM dominates with its Guardium and Cloud Pak for security offerings, as well as Informatica with its Informatica Data Privacy Management, making for a much larger, established market. 

Our mission is to help enterprises unlock the more than 90% of their data that goes unused due to data privacy and regulatory concerns. We change the game from “don’t be evil,” to “can’t be evil.” Arming enterprises with the ability to share and collaborate with that data creates opportunities that range from accelerating the creation and improving the accuracy of medical diagnoses to thwarting hackers and preventing the next big data breach. 

Today’s data privacy solutions are simply ineffective: business agreements hashed out by expensive lawyers take too long to negotiate and requires reliance on goodwill. Homomorphic encryption is slow, while secure enclaves are siloed. Masking and hashing a particular data element(s) reduces its accuracy. 

Differential privacy presents IP vulnerability, blockchain isn’t known to be future-proof, and federated learning has limited use for algorithms. Lastly, why use synthetic data when we could use real data for better results? 

TripleBlind enables enterprises to enforce compliance with any and all data privacy standards today – GDPR, HIPAA, PDPA and the myriad of state regulations popping up in the U.S. and data residency requirements in Asia. We also believe it will keep organizations in compliance with any future standards since its core architecture lets data providers share information with data users with data always remaining behind the provider’s firewall and all operations taking place behind the data user’s firewall.

We will dig deeper into the faults of these approaches and how they compare to TripleBlind. We’ll be sure to announce new blogs on our social media, so follow us on LinkedIn and Twitter. If you’re eager to learn more, schedule a call or demo for all the details at contact@tripleblind.ai.

Read the other blogs in this series:
Business Agreements
Homomorphic Encryption
Synthetic Data
Blockchain
Tokenization, Masking and Hashing
Federated Learning
Differential Privacy