Early Indication Trial Reporting Hero

TripleBlind at Work: Early Indication Trial Reporting

The process of conducting clinical trials to evaluate medical interventions currently includes the collection of an abundance of raw data, which is then formatted into structured datasets for analysis and distribution.

Following clinical trials, researchers release a Clinical Study Report (CSR), including a small subset of findings from the trial. The findings are then published in medical journals and reports for other industry experts to consume, and distributed to the public via coverage in news outlets.

This process can take anywhere from months to years, depending on the trial and the process required for the treatment, drug, or procedure to be cleared by the appropriate governing bodies. Researchers take on a considerable time and resource commitment when starting a clinical trial, and results are by no means guaranteed. According to a study by the Biotechnology Innovation Organization, just 9.6% of drugs entering Phase I clinical testing end up reaching the market, while just 30.7% of those entering Phase II and 58.1% entering Phase III result in success (link). 

A contributing factor to the low success rate of clinical trials is the limited ability for researchers to evaluate the progression and intermediate results of the trial during the midst of one of the three phases. Many trials are what are known as blind studies or double-blind studies. In a blind study, the subjects are not allowed to know whether they are in the control or treatment group. In a double-blind study, the researchers are also not allowed to know that information. 

Additionally, throughout the lifecycle of a clinical trial, researchers collect data that includes personally identifiable information (PII), or highly sensitive patient data, like name, address, date of birth, health history, as well as other data types like X-rays and genomic sequences, depending on the trial. 

For researchers, the combination of the inability to observe and compute on live data and the prevalence of sensitive, protected information make it incredibly difficult to run analyses and process data in real time, resulting in ultimately unsuccessful trials receiving more resources, time, and attention than would be efficient.

TripleBlind’s Private Data Sharing Solution allows pharmaceutical and other healthcare companies the ability to not only compliantly access this metadata during the course of the trial but also allows for early indication trial reporting, which has the potential to allow researchers to gauge how well a clinical trial is going without violating the rules for blind and double-blind studies.

TripleBlind is enabling efficiencies in clinical trials by equipping researchers with the tools they need to gather all of the important insights needed to predict how likely it will be that a given trial will result in a successful breakthrough drug, treatment, or vaccine, without revealing which participants are receiving treatment and which are not. Using this approach, researchers can compile insights into early indication trial reports which can be reviewed and shared without exposing information that would compromise the legitimacy of the trial. Access to early indication trial reporting will allow pharmaceutical companies to develop better drugs and test more efficiently. Some dead-end trials may be abandoned earlier, and resources may be allocated toward other promising approaches.

Using the tools provided by the Private Data Sharing Solution, clinical researchers can compute on data ranging in format from tabular to image and video for use in a wide range of analytics from statistical analyses to AI model training and inference. Our goal is to provide tools to all industries, including healthcare and life sciences, to accelerate innovations, reduce costs and procedural burden, and increase the level of protection on personal information. Enabling early indication reporting for clinical trials is just one prime example of the many ways we are helping organizations to modernize their data processes.

 

To learn more about TripleBlind, connect with us on Twitter and LinkedIn. Contact us at contact@tripleblind.ai to schedule a free demo.

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

Man Pondering Alternative Data

TripleBlind at Work: Alternative Data

Alternative data is increasingly valuable as many ​​banks and credit unions work with Alt Data like geolocation, IOT, satellite imagery, and ESG data to make informed investing decisions. But, with stricter data regulations, enterprises need to ensure that the data being used will not cause any increase in risk and liability. 

Use Case: How TripleBlind helps firms work with alternative data

TripleBlind works with investment companies to enhance the role of data in their service. With our Virtual Private Data Sharing Solution, investors can process data as they do today, without human analysts seeing it. There’s no additional risk or liability for either party involved; plus, investors are more likely to get access to the most impactful datasets.

Unlike synthetic data or differential privacy, models and algorithms operate on real data, resulting in a more accurate analysis without bias. Unlike manual masking, expensive anonymization steps can be skipped before processing data. The implication is that data like credit card transactions, geolocation, email receipts, point-of-sale transactions, web usage, and mobile app usage data can be used seamlessly and with the utmost privacy built in.

Alt Data Diagram

With TripleBlind, investors become equipped with better investing insights and gain access to new data previously unavailable due to privacy and trust concerns.

 

Related news: TripleBlind Accelerates Data Sharing Capabilities of Alternative Data Platform Eagle Alpha through New Partnership 

Reach out to us at contact@tripleblind.ai for any questions or a free demo! Follow us on Twitter and LinkedIn to be updated on our Use Case Blog Series. 

 

Read more:
TripleBlind At Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data

EKG resolving in TripleBlind logo

TripleBlind at Work: Mayo Clinic

Nearly a year ago, we announced our collaboration with Mayo Clinic and first demonstrated the potential of applying TripleBlind’s solution to accelerate how we develop, test, and deploy AI solutions in healthcare. 

Today, AI model developers at Mayo Clinic are working with eight hospitals across Europe, India and Israel using EKG data subject to various data privacy laws ranging in strictness. They have found that TripleBlind makes collaboration faster, easier and much cheaper, as no Personal Identifiable Information (PII) is exposed, and no manual de-identification is needed outside of TripleBlind’s automatic Blind De-Identification. The process of integrating TripleBlind with Mayo Clinic’s technology was seamless, as IT departments only needed to establish one connection with TripleBlind for each counterparty.

The collaboration utilizes TripleBlind’s novel approach to distributed artificial intelligence model training and inference, called Blind Learning. This technology was invented to allow models to be trained on datasets distributed across multiple parties who are unwilling or unable (due to privacy laws) to send the data to one centralized location where the model training occurs.

The new solution enables all the data being used for training to never be moved, yet it is still usable by data scientists, while enforcing the appropriate privacy regulations, including HIPAA, Europe’s General Data Protection Regulation (GDPR), and the strictest of data localization policies. Through this collaboration with Mayo Clinic, we’ve proven that TripleBlind allows multiple parties to collaborate in real time using real data without incurring significant speed or computational costs.

When data can be used without moving it or exposing the raw underlying information, the process of training AI models on distributed datasets becomes much simpler, faster, and cheaper. From saving intense data cleaning steps, to reduced legal process burden and liability, the benefits extend beyond making current processes easier to unlocking new opportunities to use globally sourced data.

Our collaboration with Mayo Clinic is just the start. For more information about how TripleBlind serves the healthcare industry, watch this short video. If you’re interested in TripleBlind, email us at contact@tripleblind.ai to learn how we can unlock privacy for you and schedule a free demo.

 

Follow us on Twitter and LinkedIn to be updated on our Use Case Blog Series! In the meantime, check out our other use case blogs.

TripleBlind At Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data 

 

Stylized illustration of a neural net in the shape of a brain enclosed in a protective sphere. Neurips

TripleBlind Pleased to Announce Acceptance into the NeurIPS 2021, Demo Track

We are excited to announce that our tool demonstration paper has been accepted to NeurIPS 2021, Demo Track. The paper is entitled: TripleBlind: A Privacy-Preserving Framework for Decentralized Data and Algorithms.”

The paper sheds light on the trilemma of accuracy-privacy-communication challenges for training and inference using decentralized data and algorithms. In particular, we demonstrate two major innovations of TripleBlind: Blind Learning and Privophy. Blind Learning is our training paradigm for neural networks. Privophy is a set of cryptographic protocols that enable efficient privacy-preserving processes; e.g., secure inference-as-a-service. Both innovations enable AI practitioners from all domains (i.e., physicians) to run real-time decentralized training and inference using multi-institutional datasets via a set of semi-automated, easy-to-use APIs.

Our demonstration will focus on three major concerns when it comes to privacy-preserving AI using decentralized data and algorithms: accuracy, privacy, and communication cost. We illustrate how Blind Learning can match the accuracy of centrally trained models without having to transfer any raw data outside the owner’s organization while being more communication efficient than rival solutions, including Federated Learning and Split Learning. We also demonstrate in real-time the efficiency and ease of use of our secure multi-party computation inference protocol.

The audience will also get a chance to interact with our toolset via Jupyter notebooks. The notebooks will be placed on cloud compute nodes that play the role of realistic organizations with datasets and models.

We invite you to join our demo session on Friday, Dec. 10 at 11:20-11:35 a.m. CT

For more information, visit TripleBlind’s webpage at the NeurIPS website. You can also read our tool demo proposal here

 

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The audience can use our APIs to train deep models using these datasets and also perform secure inference in real-time. We will provide five types of notebooks during our demo presentation that allow the audience to try the following tasks:

  • Training an image classifier using CIFAR-10 images distributed over three clients
  • Training a classifier using tabular data distributed over two clients
  • Training a multi-modal classifier using data of different types (text and images) distributed over two clients
  • A private set intersection to find common IDs between two clients using secure MPC
  • A secure MPC inference using a model and data owned by two different parties without compromising the privacy of neither the model nor the data
Money 2020 Recap Hero

Recap – Money 20/20 Europe

This fall, in person events were back (with the proper precautions, of course). TripleBlind recently sent COO Greg Storm, Director Partnerships Sam Abadir, and Manager of Marketing and Business Development Mitchell Roberts to Amsterdam for the largest financial services event in Europe: Money 20/20.

There, the team set up shop in this beautifully TripleBlind-branded booth (see picture below), where they met with financial services organizations dealing with data sharing issues that are either halting their current goals or preventing the pursuit of value-adding, revenue generating projects.

TripleBlind's Money 2020 Booth

At the center of many of these discussions was Greg Storm, who co-founded TripleBlind just under two years ago to address these exact issues. When he wasn’t hosting thought-provoking conversations exploring the in-and-outs of data privacy, Greg interviewed with Open Banking Excellence, a community of Open Banking and Open Finance pioneers, giving his expert opinion on what it will take to allow consumers to gain control over their financial information. You can watch the interview here.

Greg was also invited to participate in a private think tank with industry leaders. The attendees and panelists were hand-picked to discuss the topic: “The future is trustless. What do we need instead?”

Taking place in a cozy speakeasy complete with leather couches, an open bar, and a refreshing absence of glowing screens, the session resembled the Prohibition-era hidden bars in the United States, which attracted people with provisions of banned libations, but sparked incredible discussions on modern issues, as patrons were shielded for just a moment from the distractions of the outside world. 

The session was moderated by Dave Birch (Global Ambassador, Consult Hyperion), a well-known thought leader in the field of fintech. Dave asked the panelists difficult questions on the what and the how of trust as it relates to personal data. 

Co-panelists Louise Maynard Atem, Research Lead, Women in Identity; Felix Gerlach, CPO & Co-Founder, Passbase; and Katryna Dow, CEO and Founder, Meeco contributed their knowledge and experience gained from varying backgrounds to the future-facing discussion. A report documenting their discussion can be found here.

Greg Storm Talking at Money 2020

The learnings were encouraging. The group agreed that something must change to give end-consumers more control over their data. At one point Greg exclaimed, “It bothers me that, in order to view my Uber rating, which is information that should be owned by me, I have to log on to Uber to see it – and if I want to share it with someone else, Uber controls that, too.”

Other parts of the discussion focused on questioning the role of identity verification in the complex modern world. Is proving your identity sufficient or even relevant? In many cases, it is not. Sticking with the bar example, consider a bar looking to avoid serving alcohol to underage patrons. The bartenders do not need to know or care about the identity of the individual, but rather they need to know for certain that the individual is either above or below the age threshold for being served an alcoholic beverage. There are many such examples, begging the question, why is proving identity more important than proving characteristics or facts about oneself? Of course, identity verification still plays a vital role in certain scenarios, but clearly, there is work to be done in this area.

The mechanisms for achieving those results, though, are where things get interesting. How do we give data controls back to individuals without halting progress and innovation? If people have the ability to choose where their data goes and how it is used, won’t they just choose to keep it locked down and private? How do people prove characteristics about themselves without first telling you who they are? That sounds like magic.

That’s where TripleBlind comes in. Our solution allows for data to be “shared” without being transmitted, meaning insights can be gleaned from data that sits comfortably and safely behind a firewall, in the cloud or on premise. In the past, it was impossible to compute over encrypted data without having access to a secret key that decrypts the data. TripleBlind’s breakthroughs mean that the secret key is never even created, and the data is still computable! It’s not magic – it’s mathematically verified one-way encryption. And, it’s the future of data sharing.

By integrating with TripleBlind, platforms designed to allow consumer-control over their data will be equipped with the privacy-preserving tools they need to execute data operations, approved by the consumer, without transmitting any of their data to a third party. Individual features can be proven the same way an identity is proven, in a way that eliminates the trust requirement. Innovation continues, or even accelerates. Privacy is enforced.

It is clear that these problems must be solved in the near future, as the data ecosystem reaches an inflection point on the plot of innovation versus privacy. Listening to experts who tackle aspects of these issues in their daily ventures reinforces that A. the right people are putting their heads together and remaining open-minded about potential solutions, and B. we are getting very close to solving these issues.

We’re excited to be critically involved in this important moment for financial services, and can’t wait for the next opportunity to discuss data privacy with other thought leaders around the world. 

If you are interested in further discussing these topics, please reach out to us here!

TripleBlind Genetic Data Image

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.

Data Providers and Pharmaceutical Data Users Technical Diagram

 

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.

Editorial image for introductory use case blog post

TripleBlind At Work: Use Case Series

When it comes to data collaboration, the basic goal to protect sensitive data is applicable across the board. In practice, though, collaboration looks different for each industry, resulting in different requirements for ensured privacy. For example, data augmentation between industries applies to all fields including healthcare, financial services and marketing, but healthcare clients also need the added capability to broker genetic data. Additionally, regulations applied based on industry and geography make a one-size-fits-all approach to privacy impractical.

With all these variables at play, it is natural to think that there cannot possibly be one data sharing solution that can ensure all of these needs are met. However, TripleBlind has already begun to work with key players across industries and around the world, implementing a solution which has been mathematically proven to allow insights to be gathered from private data while enabling enforced compliance with all data privacy standards through strict digital rights management.

Our most recent blog series gave insight into how TripleBlind compares to competing data sharing solutions. Over the course of our next series, we will show how TripleBlind’s superior technology works in specific, real-world healthcare and financial services collaborations from industry-leading partners Mayo Clinic, Eagle Alpha and other TripleBlind clients.

TripleBlind’s groundbreaking solution enables enterprises to collaborate around previously unusable sensitive data via one-way encryption. The ability to unlock this data using TripleBlind will allow for faster and more accurate treatment and diagnosis in healthcare and quicker,more secure safety monitoring in fintech. 

Be sure to stay connected with us on our website, Twitter and LinkedIn to be updated on our Use Case Blog Series. 

Can’t wait to learn more? Contact us at contact@tripleblind.ai to learn how TripleBlind can unlock privacy for your company or to schedule a free demo.

TripleBlind Team Photo

November Update: TripleBlind Comes Together In-Person at Kansas City HQ

Our now 32-person TripleBlindian team recently came together at the company HQ for three days of planning, inspiration, and a little TopGolf. 

You may have already heard the news about TripleBlind’s $24M Series A round! We’ve been scaling and growing quickly, so we wanted to get our globally-located team members all together, in one place, for the first time.

Here’s a recap of our three-day excursion, as well as a few shoutouts to all those who helped make our team’s get together possible. 

 

Day One 

Arrival day, and board meet-and-greet dinner

Advisor Panel Discussion at Grand Street Cafe

First, we’d like to thank our board – Thad Langford, Andrew Krowne, Benjamin Bayat, Dr. John Halamka, Shail Jain, and Toby Rush – for joining us for dinner and sharing their collective vision. Our team imagines a world where “TripleBlinding” is a verb. Let’s TripleBlind It means to collaborate around private data for the benefit of all parties, while retaining complete and utter privacy.

In particular, our advisors articulated a vision of:

TripleBlinding data in healthcare to deliver advanced medicine

TripleBlinding data in pharmaceuticals to more effectively develop cures to diseases

TripleBlinding data in financial services to detect and combat fraud. 

Thank you to our advisors for their time, wisdom, and words.

 

Day Two

Board meeting, an all-team show-and-tell session, and TopGolf

Group shot of TripleBlind Team

Our co-founder and CEO, Riddhiman Das, outlined his 2022 roadmap for TripleBlind. Here are some of the top points:

  • Defining TripleBlind’s strategic market position
  • Hiring more TripleBlindians (stay tuned!)
  • Product features and enhancements (including, Blind Join – don’t know what this is? Request a demo with one of our experts)

We also had a not-so-surprise reunion of the EyeVerify crew. You may remember them as the Kansas City based startup that was acquired by Ant Financial for over $100 million

EyeVerify Team Gathered for TripleBlind

We finished the day unwinding with a little TopGolf, and we all know Gaurav gets the prize for quickest learner.

TripleBlind Team Golfing at Top Golf

Day Three

Department meetings, a museum, and three restaurants

Photo collage of TripleBlind team walking through the plaza and looking at art at the Kemper

We showed the out-of-towners the best of KC, between strolling the Plaza, touring Kemper Museum of Contemporary Art, and dining at The Carriage Club.

Tom Bergin, Software Engineer, showed us his post lunch meditation skills, while Anissa Khan, also a Software Engineer, gave us tips on our aesthetic.

Tom Bergin and Anissa Khan

Finally, we said our goodbyes as everyone headed back to their part of the globe, we’re exhilarated for what’s next – and we know each of us at TripleBlind is all-in.

Finished donuts

We’d also like to thank the employees of Grand Street Cafe for hosting us, the employees of the Overland Park Top Golf for entertaining us, and the employees of Jack Stack on The Plaza, Panera on The Plaza, Bo-Lings on The Plaza, The Parlor in the Cross-Roads, The Carriage Club, and Taj Palace on 39th Street for keeping us fed.

Thank you, Kansas City!

 

Gartner Cool Vendor Image

Gartner® Names TripleBlind a 2021 Cool Vendor in Privacy

This week, TripleBlind announced it has been named a Cool Vendor in Gartner’s October 2021 Cool Vendors in Privacy report. In the report, Gartner analysts evaluated vendors that support extensible privacy programs and how their privacy-enhancing computation (PEC) techniques affect data collaboration for users. 

“New technologies are emerging that promise a “win-win” proposition for organizations seeking to respect the privacy rights of individuals while using data for information sharing, advanced cross-entity analytics and artificial intelligence (AI) modeling,” states the report.

About TripleBlind specifically, the report states: 

“PEC techniques will be relevant in clinical trials and general healthcare, finance, insurance, parts and supply chain management, algorithm licensing and digital rights management. These can be aided by separate tools from potentially different vendors, or through singular suites like TripleBlind.”

TripleBlind being named a Cool Vendor comes on the heels of announcing both $24 million in Series A funding and a new partnership with Eagle Alpha, showing strong momentum for TripleBlind, and highlighting companies’ increased interest surrounding data collaboration via TripleBlind’s private data sharing solution.

“Being recognized as a 2021 Gartner Cool Vendor is another major milestone in solidifying our position as offering the superior technology to enable enterprises to share the estimated 43ZB of data that are not commercialized today due to regulatory concerns,” said Riddhiman Das, TripleBlind’s co-founder and CEO. “Sharing data assets is quickly becoming critical to sustained, long-term growth for businesses in every industry, including healthcare and finance in particular. Providing enterprises with a solution that allows them to collaborate while concurrently enforcing data privacy and regulatory standards could not be more important at this point in time.” 

We have many more exciting announcements in the pipeline, so be sure to keep up-to-date with TripleBlind on our website, Twitter or LinkedIn. If you are interested in learning more about TripleBlind’s efficient and cost-effective data privacy solution, contact us today for a free demo by emailing info@tripleblind.ai or visiting https://tripleblind.ai/contact/.

24 Million Series A, image of balloons and confetti

TripleBlind Secures $24 Million in Series A Funding

Earlier this month, TripleBlind announced that we closed a $24 million Series A investment led by General Catalyst and Mayo Clinic. This is a remarkable milestone for TripleBlind, less than two years into our journey. I am incredibly proud of our entire team that made this possible. 

Our Series A process happened rather suddenly when a few strategic customers started realizing the potential of TripleBlind.

 

Why General Catalyst?

It was clear that they had a strong thesis in this space – I was impressed by their knowledge of and enthusiasm for TripleBlind. Their thesis included how more and more data across industries and regulatory regimes are starting to be classified as sensitive, which inhibits the ability to share and collaborate around them.

What’s especially appealing was their overall mission of affecting a double-digit percentage reduction in the cost of U.S. healthcare. They had even written a manifesto around how data sharing is key to enabling this. If you just read the opening four paragraphs of the book, you’ll see how TripleBlind’s mission of enabling health data liquidity is key to achieving such an admirable mission.

 

Why Mayo Clinic?

The Mayo Platform is the most ambitious initiative in healthcare today. With a stated mission of delivering Mayo Clinic quality patient care globally enabled by digital health technologies. As I’ve learned more about healthcare, it’s clear that any significant progress will require close collaboration between medicine and technology, and it’s evident that Mayo is leading the world in digital health.

 

Board

We will continue to have a five-person board composed as follows:

In addition to the board directors, we’ll have the following board observers:

 

What does this mean for TripleBlind?

I first encountered HIPAA from a technology perspective while building a digital health platform for Qualcomm Wireless Health in 2011. I was surprised to learn that the most significant barriers to rapid innovation in healthcare are regulatory, not market forces or technological breakthroughs.

TripleBlind is the first and only privacy-preserving solution for patient data privacy for most global privacy regulations, including HIPAA in the U.S. and GDPR in Europe. While others have tried, failed and retreated from PHI (Protected Health Information), TripleBlind has a proven solution that ensures patient privacy is protected to the most stringent regulatory standards while enabling rapid innovation in digital health. On top of that, TripleBlind is also the only technology to ensure that the intellectual property of the algorithms are protected as they’re used by third parties as well. This positions us as a unique enabler for the next generation of breakthroughs in digital health. We’ll double down our focus on enabling digital health data platforms globally.

In addition to healthcare, we’ll continue to serve several use cases in capital markets, with the same DaaS (Data as a Service) platform-enablement product. There are significant pain points we solve for in capital markets, and it has the potential to be a much faster-moving market than healthcare. From credit underwriting to alternative data, it’s abundantly clear that market and regulatory forces are driving the need to protect consumer privacy.

In under two years, we have managed to crack the holy grail of data privacy, served reputable customers, built amazing partnerships and are backed by strong thesis-driven investors.

Thank you for joining us in our journey. I can’t wait to see what global innovators will do with the rapid pace of innovation enabled by TripleBlind.

I am excited to build the future with you.