Accenture Makes Strategic Investment in TripleBlind to Bolster Data Privacy and Increase Data Collaboration Opportunities
Startup joins Accenture Ventures’ Project Spotlight program to accelerate go-to-market strategy
Sharing and extracting insights from sensitive data, such as patient records or tax returns can help accelerate cures for diseases or reduce financial crimes, yet, concerns around data privacy and governments regulations have prevented many companies from identifying these insights to date. TripleBlind helps enterprises share sensitive information with their stakeholders more effectively – without ever decrypting the data, helping them comply with regulatory requirements.
“Organizations can yield valuable insights and unlock trapped value by combining and collaborating around large volumes and different types of data, but in order to do this they need to trust that the privacy of that data is protected,” said Shail Jain, global lead for Data & AI Group for Accenture Technology. “We believe that TripleBlind not only has the capabilities to facilitate collaborative data exchanges, but to also give organizations confidence that data privacy remains intact.”
TripleBlind is now part of Accenture Ventures’ Project Spotlight, a deeply immersive engagement and investment program that targets emerging technology software startups to help the Global 2000 embrace the power of change and fill strategic innovation gaps. Project Spotlight offers extensive access to Accenture’s deep domain expertise and its enterprise clients, to help startups harness human creativity and deliver on the promise of their technology. Through the program, TripleBlind will co-innovate with Accenture at its Innovation Hubs, Labs and Liquid Studios, working with subject matter experts to bring its solutions to market more quickly and more effectively.
Accenture invests in TripleBlind, a data privacy and virtual clean room solution provider.
“The global market for big data and business analytics is projected to reach more than $500 billion by 2026. As that market grows, the pressure within enterprises to share data to uncover new revenue opportunities and gain competitive advantage will grow as well,” said Riddhiman Das, co-founder and CEO of TripleBlind. “TripleBlind’s next-generation cryptographic, efficient and scalable data privacy and virtual clean room solution can replace ineffective workarounds like complex legal contracts, data anonymization or deidentification, and other technologies such as homomorphic encryption, while helping to avoid regulatory statutes and data residency violations. Accenture Ventures’ investment and our participation in its Project Spotlight program will advance and accelerate our ability to help enterprises harness the potential of sensitive data.”
Tom Lounibos, managing director, Accenture Ventures, added, “Our investment in TripleBlind demonstrates Accenture Ventures’ commitment to cultivating the latest technologies, enhanced by human ingenuity, that solve for our clients’ most critical business needs. We believe that TripleBlind offers our clients a key to enhancing data privacy while ensuring regulatory compliance – a major challenge in today’s environment.”
TripleBlind is the latest addition to the investment portfolio of Accenture Ventures, which is focused on investing in companies that create or apply disruptive enterprise technologies.
Terms of the investment were not disclosed.
TripleBlind was founded in 2019 and is headquartered in Kansas City. It’s new data privacy and virtual clean room solution enables enterprises to leverage regulated data, such as PII and PHI, and enterprise-sensitive data without violating GDPR, HIPAA, the California Consumer Privacy Act (CCPA), data residency and other standards. For more information, please visit tripleblind.ai.
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My name’s Riddhiman Das. I am CEO and co-founder of Triple Blind, and Triple Blind does privacy as a service. What that means is we enable parties to work together on data that’s sensitive because of regulatory reasons, or competitive reasons, or just plain consumer privacy reasons. And we allow those data sets to be used without violating their privacy. What motivated me to found Triple Blind was the fact that we have so much data that is being stored by enterprises, and in large companies, and banks, and healthcare institutions that just never sees the light of day. The problem Triple Blind solve for companies is one of two, typically.
One, is either providing access to data that has historically been inaccessible because of the sensitivity around that dataset. On the other side, there are companies that have lots of data that they know is valuable to third parties, but they don’t allow them to access because of the sensitivity around that data. So, this, essentially, Triple Blind allows the latent datasets of those organizations to be monetized safely and securely without exposing them to any regulatory violations or compliance hurdles.
So, how Triple Blind is better than existing solutions is the ability to operate at scale on any kind of data on any operation. What that means is we actually can handle big data. We can do deep neural network training. We can do data processing. We can just do statistical modeling on any kind of data. This can be voice, text, video, tabular data, or images. The reason why it’s faster is because it’s not a CPU-bound isomorphic encryption. It is not hardware-bound like secure enclaves, and you don’t have to use an approximation of the data like synthetic data. This is applicable in medical fields or anywhere where you need to use real data and actually use the outliers in your analysis, as opposed to masking the outliers as you would in synthetic data.
Triple Blind’s mathematical breakthrough eliminates the need for those oblivious transfers. You accumulate less errors as you are progressing. And at the end, we offer the exact same accuracy and precision as if you were operating on unencrypted data. And it happens significantly faster than was ever possible before. In a particular healthcare use case, we enabled our healthcare partner to build an algorithm using EKGs of the heart. In this particular example, we worked with EKGs. We enabled our healthcare partner to build an algorithm that detects atrial fibrillation of the heart 13 years before their best cardiologist’s manual diagnosis. And this algorithm had significant IP and lots of… They spent millions of dollars in building this algorithm, so we were able to encrypt this algorithm using a novel invention by Triple Blind, where this protected the intellectual property and the algorithm as it was licensed to third party countries. And the specific privacy regulation in those countries was also respected because the data that was sent to this algorithm was encrypted and de-identified at the time of sending.
In financial services, what typically happens is that there are data silos. No individual, typically banks, or uses just one financial institution. We may have a credit card at a bank A, we may have a checking account at bank B, we may have an investment account and investment brokers at C. And in those cases, if you just build your anti-money laundering, or anti-terrorism funding, or credit card fraud examples with one of those datasets, you are potentially missing out on the complete financial picture of the person. We’ve been able to protect the privacy of the bank’s customer data as well as the bank’s proprietary information about that customer, while still allowing the data analyst company to have a more complete financial picture of the person, and build a more accurate and a more generalizable credit card fraud detection algorithm.
We’re very excited about Accenture’s investment because it shows that this technology is not just a scientific experiment in a lab. It is a real-world product that can be deployed today. So, Accenture’s investment reinforces to us that our particular technology approach here is credible and is market-ready so that it can be deployed at not just the customers that we sell to directly, but with Accenture’s channel partner here. It really enhances our go-to-market journey. It’s really unusual for Accenture to invest in what is our pre-seed round. We are the youngest investment ever by Accenture in the earliest stages of a company, which really shows that the conviction they had in Triple Blind and its ability to solve this problem.
Some of our angels include Brian McClendon, who was the founder of Google Maps, and Google Earth, and the founder of Uber Maps as well. They include Manik Gupta, the Chief Product Officer of Uber, as well as people that have started and sold technology high-scale businesses in the past. In addition to that, we have had participation from Flyover Capital, a partner that we’re really excited about. The fundamental design principle that we’ve used in Triple Blind is to essentially be the Stripe of privacy. Triple Blind, similarly, is accessible to any engineer or product people with very little overhead. You can get up and rolling in under a day. If you want to try this, and you think Triple Blind can help you access more data, or safely, securely, let others access your data or comply with privacy regulation via technology, please reach out at tripleblind.ai. We’ve got wonderful use cases that you can study to see how it might be applicable in your use case. You can get a demo or you can just drop us a note at email@example.com.
Q. Why is Accenture Ventures interested in investing in data privacy technology?
A. Accenture Ventures is focused on cultivating emerging technologies that solve for our clients’ most critical business needs. Many global enterprises are struggling to unlock trapped value in their data and data privacy concerns, and regulatory mandates are a huge part of that. Global organizations need to be able to effectively use their data and that includes collaborating with other companies, partners and competitors. Advanced data privacy solutions close that gap.
Q. Why did you select TripleBlind?
A. TripleBlind offers a high degree of privacy and interoperability, without compromising flexibility or accuracy of the operations performed on the data. From training deep nets on images, to big data processing, TripleBlind can help capture value while enhancing compliance with global privacy regulations.
Q. What aspects of TripleBlind’s technology are unique?
A. Many companies track and monetize sensitive personal information, but doing so securely – particularly when sharing that data with other companies — is a significant challenge. TripleBlind’s approach allows enterprises to share the information and insights of their data, without sharing the data itself. This is a much more effective and elegant solution then complex contracts and other types of technologies available today.
“TripleBlind’s approach allows enterprises to share the information and insights of their data, without sharing the data itself. This is a much more effective and elegant solution then complex contracts and other types of technologies available today. ”
Q. In addition to providing pre-seed funding, how will Accenture Ventures support TripleBlind?
A. As part of Accenture Ventures’ Project Spotlight program, TripleBlind will get extensive access to our technology domain experience. We’ll co-innovate with them at our innovation hubs, labs and liquid studios to help them refine their product-market fit. We’ll also connect TripleBlind with other companies in the Accenture Ventures ecosystem, both to help them sell in their solutions and to create technology and distribution partnerships. In fact, TripleBlind is already talking to another Project Spotlight company we invested in this summer that has a complementary solution.
In about 10 days, we will have a significant announcement. The VC arm of a leading global professional services company will invest in TripleBlind and greatly accelerate our growth by connecting us to their ecosystem of companies and expertise.
Our new investor was eager to enter the data privacy space and organized a global review of the companies in the space. After a thorough review, they chose TripleBlind, noting that our solution is much more effective than the workarounds available today – complex legal contracts, data anonymization/de-identification and cumbersome alternative technologies, such as homomorphic encryption. For an example of how our solution works, please have a look at our recent blog.
Some of the obvious problems with these workarounds include:
- Complex legal contracts often contain terms that are limiting and/or include liability clauses that are so onerous that companies are unwilling to enter into them, thus negotiations are typically expensive and require months to complete, despite the potential collaboration benefits
- Data anonymization/deidentification is hard to execute well. It is ineffective if it doesn’t meet three criteria: individualization (it must not be possible to identify an individual), correlation (it must not be possible to cross-check multiple data sets to identify an individual), and inference (it must not be possible to deduce information about an individual from the data set)
- Other technologies: Homomorphic encryption is a slow process that dramatically taxes compute performance. Differential privacy introduces inaccuracies to the eventual calculation. Secure enclaves are hardware dependent, do not support AI/ML at scale, are susceptible to known hacker attacks and not easy to update.
Shortly after announcing this large investor, we plan to announce new funding by several experienced VC firms and veteran angel investors. In fact, this pre-seed round was oversubscribed.
What is generating so much interest in our solution? Today, IBM estimates that enterprises fail to utilize as much as 93% of their data to generate incremental revenue by collaborating with customers, partners and even competitors. This is typically regulated data, such as PII and PHI, or mission-critical enterprise data such as tax returns and banking transactions. Our solution enables enterprises to unlock the information and insights within this data (note I didn’t state the data itself) while remaining in compliance with GDPR, HIPAA, CCPA (California Consumer Privacy Act) and similar regulations.
At TripleBlind, we think the better answer is mathematically enforced cryptography that doesn’t rely on laws or outdated technology, but rather privacy that is built into the protocol. We are working to “build in” privacy preservation and we want to give enterprises the keys to either lock or unlock their data as they see fit.
We are excited to apply these investments to complete product development and testing, and bring our solution to the over $500 billion data analytics market.
Stay tuned for upcoming announcements of major new customers!