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TripleBlind at Work: Diagnostics Remote Delivery

Hospitals with highly advanced diagnostics algorithms can broaden their impact through licensing or otherwise allowing third party use of their AI algorithms without fear of reverse engineering. TripleBlind makes this possible so that remote healthcare diagnostics centers can perform faster diagnoses on real patient data.

Diagnostic Remote Delivery Diagram: Hospital delivers algorithm to Diagnostic Center for x-ray analysis

TripleBlind enables diagnostic algorithms to operate on one-way encrypted and de-identified data. Any data, including X-ray images and EKGs, can be used at its highest resolution without incurring an accuracy penalty. The solution provides many advantages over the five methods for data anonymization most frequently utilized today. Blind de-identification does not alter the fidelity of the data, while:

  • K-anonymization alters the fidelity of the data through two means: suppression (data masking); certain values of the attributes are replaced by an asterisk. All or some values of a column may be replaced by an asterisk; or generalization; individual values of attributes are replaced with a broader category, e.g., the value 19 might be replaced with <20,
  • Pseudonymization replaces private identifiers with fake identifiers or pseudonyms,
  • Data swapping (shuffling or permutation) rearranges the dataset attribute values, so they do not correspond with the original records,
  • Data perturbation modifies the original data set by rounding numbers and adding random noise, also known as differential privacy,
  • Synthetic data is often used in place of altering the original dataset or using it as is and risking privacy, but even the best synthetic data is still a replica of the general properties of the original data.

TripleBlind allows operations on data in real-time without needing to generate an anonymized basket of data that is a snapshot of the past. The path from data collection to data usage is significantly faster, cheaper and seamless using Blind De-identification. Fewer data preparation steps translate to lower data project costs, less legal paperwork and more powerful insights that use the complete, unaltered dataset in the most private way currently possible.

In addition to offering the best privacy for data in-use, our solution importantly and revolutionarily protects the intellectual property of algorithms, with a breakthrough one-way algorithm encryption capability which protects algorithms-in-use from common attacks aimed at reverse engineering an algorithm for reconstructing the data that went into training an AI model. 

Now, the world’s best diagnostic algorithms can be put to use for remote diagnostics, without exposing valuable IP, while preserving HIPAA compliance via TripleBlind’s Private Data Sharing Solution. 

 

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
TripleBlind at Work: Early Indication Trial Reporting

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TripleBlind at Work: Retail Store Recommender System

Our Use Case blog series has shown how TripleBlind’s Blind Exchange API can facilitate collaborative breakthroughs for healthcare and financial institutions, allowing the sharing of insights derived from sensitive data that has historically been guarded by privacy regulations. TripleBlind’s Blind AI Tools also allow collaboration in everyday scenarios that directly benefit consumers, while respecting and enforcing their rights to privacy.

Two industries that stand to greatly benefit from this new set of tools are the consumer packaged goods (CPG) and retail industries. Apart from usage to manage functions that directly benefit consumers, like more accurate inventory management and personalized coupon recommenders, retail outlets can collaborate with other outlets they share customers with to better predict consumer buying behavior.

Customers often frequent multiple retail outlets to do their shopping due to preferences in different products, availability, price promotions and more; however, no two consumers follow the same path to purchase. Using data including point-of-sale (POS), customer transaction, geolocation, online retail activity, and more, retailers can better understand customers’ shopping patterns and interests, making it easier to predict buying cycles, anticipate repeat purchases, and ensure the right goods are always in stock.

With a conglomerate of each customer’s buying personalities across brick-and-mortar stores and online, outlets can provide more personalized recommendations and reduce spam. Customers and retailers also benefit from a security standpoint, privately combining activity from multiple stores can be used to logically detect suspicious activity or potential credit card fraud.

Most people agree that targeted advertisements can be uncomfortable. While we would all likely enjoy more specialized promotions and deals, currently we are often left with a feeling that our privacy was potentially violated in the process of delivering ads that, say, relate directly to a casual conversation you were having with a friend. TripleBlind offers a solution which gives a win-win option for consumers and retailers. Consumers’ privacy is protected, as retailers and advertisers never get to learn any private information about individuals, and retailers get to deliver specialized ads to their target audiences.

Centralizing data across companies can be time-consuming and expensive, but because TripleBlind’s APIs present themselves similarly to widely-used frameworks like Pandas, PyTorch, Scikit-Learn and Tensorflow, only a few lines of code are needed to add privacy to an enterprise’s existing infrastructure, saving retail companies significant IT spend and reducing time-to-insights during collaboration.

 

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
TripleBlind at Work: Early Indication Trial Reporting 

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KC INNO: Startups To Watch

https://www.bizjournals.com/kansascity/inno/stories/news/2022/01/28/kc-inno-10-startups-to-watch-2022.html

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TripleBlind Expands Board of Directors Composed of Industry Luminaries to Accelerate Growth Within Healthcare, Life Sciences and Financial Services Markets

KANSAS CITY, Mo., Jan. 25, 2022 (GLOBE NEWSWIRE) — TripleBlind, creator of the most complete and scalable solution for privacy enhancing computation, which unlocks the intellectual property value of data, while preserving privacy and enforcing compliance with HIPAA and GDPR, announced today additions to its board of directors that will reinforce the company’s leadership position in supporting healthcare, financial services and other enterprises to utilize the 43ZB of data that is currently unutilized.

“Effective collecting, managing and utilizing data is the lifeblood of many industries today. Unlocking that data while enforcing data privacy is essential to long-term sustained growth,” said Riddhiman Das, co-founder and CEO of TripleBlind. “We have carefully selected this board of directors to provide the expertise and market experience we need to reinforce our position as the most effective private data sharing solution available, and expand our focus beyond healthcare into financial services, and other industries where innovative utilization of data assets is essential.”

TripleBlind’s newly expanded board of directors includes:

Ben Bayat, Managing Director, NextGen Venture Partners – Bayat opened NextGen’s west coast office and leads investing for NextGen’s Venture Fund, focused on early stage technology investments.

Quentin Clark, Managing Director, General Catalyst – Clark has more than 25 years experience in product, technology and SaaS space, and has invested in more than 50 companies. Previously, he held management positions at Dropbox, Microsoft and SAP.

John Halamka, M.D., President, Mayo Clinic Platform – Dr. Halamka leads a portfolio of platform businesses focused on transforming healthcare by leveraging artificial intelligence, connected healthcare devices and a network of trusted partners. Trained in emergency medicine and medical informatics, Dr. Halamka has been developing and implementing healthcare information strategy and policy for more than 25 years. He joined the Board as an observer.

Shail Jain, Data & AI lead for Accenture Cloud First – Jain is responsible for the growth of Accenture data and AI expertise within Accenture Cloud First, a multi-service group of 100,000+ cloud professionals dedicated to rapidly expanding Accenture’s cloud service capabilities and offerings. Prior to joining Accenture, Jain served as CEO of Knowledgent, a leading data and analytics company that Accenture acquired in 2018. He is a serial entrepreneur and has built three technology services companies over the last 25 years. He joined the Board as an observer.

Andrew Krowne, Managing Director, Dolby Family Ventures – Krowne manages the early-stage venture capital firm’s investments and builds on the Dolby family’s commitment to discovering and supporting visionaries and entrepreneurs.

Thad Langford, Managing Partner, Flyover Capital – Langford is a TripleBlind Board observer. Before Flyover, Langford was the CEO of Zave Networks (acquired by Google), a venture-backed marketing attribution platform, based in Kansas City. He is currently focused on creating the next generation of technology success stories in areas outside of the traditional tech hubs.

Toby Rush, Entrepreneur and CEO – Rush was previously CEO of Smart Warehousing, which has built a platform that accelerates omnichannel fulfillment, enabling the company to take advantage of the current disruption in retail. He was the founder of EyeVerify and sold the company to Alibaba in 2016 where he led the biometrics and identity platform. Rush joined the Board as an observer.

“TripleBlind offers a way for enterprises to responsibly do more with their data, which is a growing challenge that has an exponential impact on agility in the business. By opening up an enterprise’s data while ensuring security, privacy and compliance, TripleBlind can empower these businesses in a way that is still in alignment with their stakeholders,” said Quentin Clark, Managing Director, General Catalyst. “We believe that facilitating intelligent data sharing and collaboration will be a core strategy when it comes to building responsibly innovative enterprises of the future. Triple Blind perfectly aligns with General Catalyst’s mission, and we are excited to partner with them on their journey.”

TripleBlind recently announced a $24 million oversubscribed Series A funding round led by General Catalyst and Mayo Clinic. The round follows TripleBlind’s seed raise of $8.2 million announced in March 2021.

TripleBlind creates new opportunities for enterprises through use cases such as:

  • Healthcare organizations build more diverse patient data sets enabling development of highly accurate diagnostic algorithms, develop better treatments and drugs, and other patient care initiatives
  • Financial institutions share data to create a comprehensive view of consumers and create more effective anti-fraud and anti-money laundering strategies
  • Organizations where data is siloed collaborate on joint initiatives with customers, vendors and partners and present a unified brand experience
  • Data marketplaces across all industries can allow computations to occur on data assets listed on their platform without transaction the raw data, meaning data is ready for ”market” immediately during collaboration or company mergers
  • Researchers conducting double-blind experiments can gain an early indication of the probabilistic certainty that the trial will succeed or fail, without violating the rules of double-blindedness.

About 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 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. TripleBlind enforces international and regional data privacy standards, including HIPAA, GDPR, PDPR, and CCPA.

Helpful links:
TripleBlind Two Minute Overview Video
Background on Competing Solutions

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, contact@tripleblind.com.

About General Catalyst
General Catalyst is a venture capital firm that invests in powerful, positive change that endures — for our entrepreneurs, our investors, our people, and society. We support founders with a long-term view who challenge the status quo, partnering with them from seed to growth stage and beyond to build companies that withstand the test of time. With offices in San Francisco, Palo Alto, New York City, London, and Boston, the firm has helped support the growth of businesses such as: Airbnb, Deliveroo, Guild, Gusto, Hubspot, Illumio, Lemonade, Livongo, Oscar, Samsara, Snap, Stripe, and Warby Parker. For more: www.generalcatalyst.com.

Contact
mediainquiries@tripleblind.com

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Experts in AI: Private Data Sharing with TripleBlind

https://aibusiness.com/video.asp?section_id=788&doc_id=774640&

Webinar: Collaborating with AI Tools in Financial Services

Webinar: Collaborating with AI Tools in Financial Services

Risks and regulations dramatically slow the use of AI tools in financial services, especially if…

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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

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Tech Talk: Triple Blind provides privacy for data and algorithms, allowing collaboration with private and sensitive data.

https://www.thebanker.com/video/v/6290882176001/tech-talk-triple-blind-provides-privacy-for-data-and-algorithms-allowing-collaboration-with-private-and-sensitive-data

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TripleBlind Thought Leaders Share Expert Insight at Industry Events

KANSAS CITY, MO., Jan. 3, 2022TripleBlind, the private data sharing solution that enables enterprises to freely collaborate using their most sensitive data and algorithms without ever compromising privacy, will share industry insights and trends at two upcoming events.

TripleBlind will participate in Collaborating with AI Tools in Financial Services:

  • Collaborating with AI Tools in Financial Services, Tuesday, Jan. 18, 2022 at 8 p.m. CT, Virtual.

TripleBlind and UnionBank of the Philippines will host a joint webinar on why enterprises need to be prioritizing collaboration around AI tools and how to do it securely and privately.

In this free webinar, the below thought leaders will discuss challenges in collaborating with AI in financial services, opportunities in operationalizing AI in private financial data, and how to securely and privately collaborate around private data using AI tools:

    • Chris Barnett, VP of Partnerships & Licensing at TripleBlind
    • David Hardoon, Senior Advisor for Data and Artificial Intelligence at UnionBank of the Philippines
    • Dr. Adrienne Heinrich, AI CoE Head, Vice President at UnionBank of the Philippines
    • Samir Mohan, Partnership Engineer at TripleBlind

Click here to register.

 

About 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. TripleBlind enforces international and regional data privacy standards, including HIPAA, GDPR, PDPR, and CCPA.

 

Helpful links:
TripleBlind Two Minute Overview Video
Background on Competing Solutions

 

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, contact@tripleblind.ai.


Contact

mediainquiries@tripleblind.ai