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.
With TripleBlind, investors become equipped with better investing insights and gain access to new data previously unavailable due to privacy and trust concerns.
https://tripleblind.ai/wp-content/uploads/2021/12/AltDataUseCase.png7201080TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logofirstname.lastname@example.orgTripleBlind2021-12-20 10:07:022022-01-10 11:09:38TripleBlind at Work: Alternative Data
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 email@example.com to learn how we can unlock privacy for you and schedule a free demo.
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https://tripleblind.ai/wp-content/uploads/2021/12/MayoClinicUseCase.png7201080TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logofirstname.lastname@example.orgTripleBlind2021-12-13 18:40:232022-01-10 11:09:22TripleBlind at Work: Mayo Clinic
Follows recent announcements of $24 million funding and being named a Gartner “Cool Vendor”
KANSAS CITY, MO – December 9, 2021 –TripleBlind, the private data sharing solution that allows enterprises to collaborate via its Blind Virtual Exchange API which ensures raw data is never moved or exposed, has announced new hires to its leadership team. The following new hires bring expertise to the company that will expand partnership, collaboration and business growth for TripleBlind across industries.
These appointments follow several recent TripleBlind announcements, including raising $24 million in an oversubscribed Series A funding round led by General Catalyst and Mayo Clinic and being acclaimed as a “Cool Vendor” in Data Privacy by industry analyst firm Gartner.
Jay Smilyk joins TripleBlind as Chief Revenue Officer (CRO). He has held C-level and leadership positions at multiple software and security companies, including Sepio Systems, Vectra Networks, Safend/Supercom and Cynet, and has more than 20 years of sales management experience. At TripleBlind, Jay will oversee company revenue growth and management, sales and marketing.
Suraj Kapa is TripleBlind’s new Senior Vice President of Healthcare, joining after serving for more than eight years in several senior positions at Mayo Clinic, most recently as Medical Director, AI for Knowledge Management and Delivery. He brings more than 10 years of experience practicing as a cardiac electrophysiologist and working with AI for healthcare. In his new position, he will oversee all healthcare business unit operations and own the processes and functions related to the TripleBlind Healthcare Business Unit.
Tim Massey joins TripleBlind as Vice President of Product & Customer Success. Leveraging more than three decades of experience in technology working with large enterprises and startup companies, Tim will oversee the development and execution of TripleBlind’s product strategy and ensure that customers realize maximum value through their use of TripleBlind solutions. He is highly regarded as a thought leader in the industry through his time holding leadership positions at multiple technology companies, including successful Kansas City startups EyeVerify and Handmark.
Siddhartha Banerjeehas been named TripleBlind’s Director of Business Development, focusing on financial services and capital markets. He previously held senior sales positions at Databricks, Wipro and Compact Solutions (acquired by Informatica). Sid brings more than 25 years of experience in global sales, consulting and leadership to the team.
“These strategic hires will allow TripleBlind to accelerate the commercial success our private data sharing solution is achieving today. Each executive brings extensive experience that will propel us to unlock new opportunities in financial services and healthcare, as well as additional markets,” said Riddhiman Das, CEO and co-founder of TripleBlind.
TripleBlind offers proprietary cryptographically-enforced privacy for data and algorithms, allowing institutions to collaborate around the most private and sensitive data without it ever being decrypted or leaving their firewall. TripleBlind provides one-way encryption and allows only authorized operations on any type of data, any algorithm, computable by third parties in real-time. TripleBlind never hosts or accesses shared data.
TripleBlind’s Private Data Sharing Solution unlocks the estimated 43ZB of data that are not commercialized today. The company’s patented breakthroughs in advanced mathematics enable organizations to secure larger and more diverse data sets for innovating enhanced algorithms for medical diagnoses and improved anti-fraud initiatives in financial services. It is the only technology that enforces all international and regional data privacy standards, including HIPAA, GDPR, PDPR, and CCPA.
TripleBlind technology significantly differs from existing solutions and is not based on homomorphic encryption, secure enclaves, tokenization/masking/hashing and differential privacy, synthetic data, federated learning and blockchain. For an overview, a live demo, or a one-hour hands-on workshop, email@example.com.
https://tripleblind.ai/wp-content/uploads/2021/12/ExpandsLeadership.png7201080TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logofirstname.lastname@example.orgTripleBlind2021-12-09 20:09:502021-12-09 20:10:13TripleBlind Expands Leadership Team as its Private Data Sharing Solution Achieves Commercial Success
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.
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
https://tripleblind.ai/wp-content/uploads/2021/11/NeurIps@2x.png14412161TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logoemail@example.comTripleBlind2021-12-08 21:09:032021-12-10 01:43:44TripleBlind Pleased to Announce Acceptance into the NeurIPS 2021, Demo Track
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.
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.
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!
https://tripleblind.ai/wp-content/uploads/2021/12/Money2020Recap@2x.png14412161Mitchell Robertshttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logofirstname.lastname@example.orgMitchell Roberts2021-12-06 22:28:442021-12-07 15:44:52Recap – Money 20/20 Europe
Releases Solution Upgrade to Address Use Cases in Financial Services, Healthcare and Life Sciences
KANSAS CITY, MO., Dec. 6, 2021 – TripleBlind, the virtual private data sharing solution that accelerates the ability of enterprises to commercialize data while preserving privacy and enforcing compliance with all worldwide data privacy regulations, has updated and expanded its solution offering to address additional use cases.
With TripleBlind, financial institutions can share data to create a comprehensive view of consumers and create more effective anti-fraud and anti-money laundering strategies. Healthcare organizations can build more diverse patient data sets enabling development of highly accurate diagnostic algorithms, develop better treatments and drugs.
TripleBlind’s latest upgrade is built to allow data projects to progress as usual, but with an added layer of privacy. The main focus of the solution design is the user experience; the end user should never have to be an expert in the underlying cryptographic primitives in order to use the technology.
Since Python and R are the chosen languages for the majority of machine learning and data scientist users, TripleBlind’s Blind Data Tools and Blind AI Tools support these languages and the powerful data analysis and machine learning libraries made available through each. Using TripleBlind’s Blind Data Tools,the user is able to perform all the typical tasks involved in the data pre-processing pipeline, as well as perform tasks including logical aggregations, redactions, and Blind Sampling to ensure the quality of the data before usage.
TripleBlind’s Blind AI Tools support training a wide range of AI network architectures so that any desired architecture can be built on nearly any data type, without needing to “see” the data. TripleBlind users can also run Blind Inferences on trained models without ever physically exchanging the data or the model, allowing deployment of AI models across the globe.
With TripleBlind’s Blind Compute functionality, data and algorithms stay private and remain usable on automatically de-identified data. The company’s novel cryptographic method of splitting calculations in parts that can be run in parallel saves storage capacity and time, while further boosting privacy. Since TripleBlind’s APIs present themselves as similar to well-known frameworks such as Pandas, PyTorch, Scikit-Learn and Tensorflow, any number of them can be easily implemented and used by third parties. Only a few lines of code are needed to add privacy to an enterprise’s existing infrastructure.
With TripleBlind’s Blind Query, enterprises can bring together the data they need without privacy risk to them or their customers. Blind Query provides several ways to search, join and analyze data from disparate sources, while TripleBlind’s Blind Algorithm Tools enable the easy distribution of algorithms while maintaining strict security and theft prevention, enabling greater and safer collaboration.
For even greater accessibility, TripleBlind’s updated web user interface now allows enterprises to define and run a no-code drag-and-drop version of Blind Join. It logically aggregates two or more datasets for analysis in real time, without the user needing to look at a single line of code with all the same privacy benefits applied.
“Our Blind Virtual Exchange API is a simple code snippet that ensures raw data is never moved or exposed. Our API enables collaboration between entities, allowing operation on every detail of that data without ever transmitting unencrypted or non-aggregate information, while also enforcing permissions and providing an audit trail of use,” said Steve Penrod, Vice President of Product Development at TripleBlind. “Our Blind Algorithm Tools will empower financial and healthcare enterprises to easily distribute algorithms while maintaining strict security and theft prevention.”
The TripleBlind Private Data Sharing Solution unlocks the estimated 43ZB of data stored by enterprises today that are inaccessible and not commercialized due to privacy concerns, operational complexity and regulations. The company’s patented breakthroughs in advanced mathematics enable organizations to secure larger and more diverse data sets for innovating enhanced algorithms for medical diagnoses and improved anti-fraud initiatives in financial services. It is the only technology that enables enterprises to rapidly commercialize data while maintaining compute performance; enabling analysis of all data types, such as PII, PHI, genomic data, images, and confidential financial records; and enforcing all international and regional data privacy standards, including HIPAA, GDPR, PDPR and CCPA.
TripleBlind is superior to existing solutions such as homomorphic encryption (slows compute performance), secure enclaves (siloes data), tokenization/masking/hashing and differential privacy (reduces accuracy), synthetic data (not real data), federated learning (limited use for algorithms), confidential computing (requires data centralization) and blockchain (not interoperable). Innovators including Accenture, the Mayo Clinic, and Snowflake trust TripleBlind to protect sensitive data. For an overview, a live demo or a one-hour hands on workshop, email@example.com.
https://tripleblind.ai/wp-content/uploads/2021/12/ProductUpdate.png7201080TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logofirstname.lastname@example.orgTripleBlind2021-12-06 21:06:442021-12-06 21:07:08TripleBlind Updates Virtual Private Data Sharing Solution to Enhance Performance and Simplify the User Experience
In this webinar TripleBlind, the company that offers a proprietary cryptographically-enforced data privacy solution, and Eagle Alpha, the leading external data aggregators, will discuss why and how corporates should externally consider monetizing their data.
The topics to be covered include:
An overview of the external data market and opportunities (also known as alternative data)
First-hand accounts of successful data monetization strategies
The importance of data security & compliance procedures when monetizing data
How TripleBlind helps firms safely monetize data
How the combination of Eagle Alpha and TripleBlind can supercharge your external data monetization efforts
For this webinar we will also be joined by Toby Dayton, CEO of employment data vendor LinkUp, and Enrique Garcia-Ayesta, CEO of telco data vendor Apeiron Insight.
https://tripleblind.ai/wp-content/uploads/2021/12/EagleAlpha@2x.png14412161TripleBlindhttps://tripleblind.ai/wp-content/uploads/2020/05/TB-Logoemail@example.comTripleBlind2021-12-01 22:08:582021-12-01 22:19:02Webinar: The Data Monetization Opportunity (and How to do it Securely!)
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