Recently, we came across this insightful article from the World Economic Forum, “What if we get tech right?” which covers emerging technologies, but one part in particular caught our attention. Benjamin Haddad, Director of Technology Innovation at Accenture, and Algirde Pipikaite, Lead, Strategic Initiatives for the Centre for Cybersecurity at World Economic Forum stressed the importance of designing data architecture embedded with privacy and security. Today, we often rely on ethics when it comes to data compliance, putting too much personal information at risk. But as we move closer and closer to data liquidity, laws are being proposed to control and protect sensitive data.
At TripleBlind, developing advanced mathematics to create an entirely new, comprehensive and streamlined approach to data privacy is our reason for being. TripleBlind’s cryptographic digital rights management allows fine-grained control of data and algorithm interactions with cryptographic consent needed for every operation. We never decrypt or copy the data and algorithms, meaning everyone involved, including data scientists or TripleBlind ourselves, never see the raw data and algorithms. Everything remains confidential and secure without losing the valuable assets of the data itself.
“This political debate over data residency is expected to gain as much importance as the one on foreign ownership of a country’s sovereign debt.”
TripleBlind also allows computations to be done on enterprise-wide global data while enforcing data residency regulations. We know data residency laws vary from country to country and keeping track and maintaining compliance is difficult and costly. TripleBlind enables convenient access to global data silos, all while maintaining compliance with even the strictest of data laws.
As we head toward a future where big data is going to be key to unlocking countless advancements and insights, we have to put the privacy and security of that data first. TripleBlind is at the forefront of addressing this issue and we will continue to set the industry standard for data sharing. To keep up to date with us, subscribe to our newsletter and follow us on LinkedIn and Twitter!
https://tripleblind.ai/wp-content/uploads/2021/05/IndustrialRev@2x.png14402161TripleBlindhttps://tripleblind.ai/wp-content/uploads/2022/02/TripleBlind-Logo-H-FullColor@4x.pngTripleBlind2021-05-26 21:25:352021-10-27 14:37:04Responsible Technology is Leading the Fourth Industrial Revolution
Enforces HIPAA, GDPR and other Regulatory Standards, Built on One-Way Encryption, Eliminates Potential for Patient Identification
KANSAS CITY, MO., May 25, 2021 – The solutions available today to share regulated data while remaining in compliance with HIPAA, GDPR and other standards are typically slow and expensive, and it can also be unclear that all the data was actually de-identified. As a result, many organizations continue to conduct research and data analysis as well as develop algorithms with limited data sets, impacting the efficacy of these operations. And, enormous amounts of healthcare data remain unutilized due to privacy concerns.
TripleBlind has created a highly innovative approach to data de-identification via one-way encryption that allows all data attributes to be used, even at the patient level, while eliminating any possibility of the data user learning anything about the patient’s identity. Its patented breakthroughs in advanced mathematics give healthcare organizations the ability to share PHI, health records, genomic and other data, unlocking the ability to conduct a wide range of data operations, such as data analysis, algorithm development and algorithm validation. The TripleBlind solution effectively de-identifies genomic data, without impacting the resolution of the underlying data.
“We selected TripleBlind as our data privacy partner due to its novel approach for protecting sensitive patient and related data. Its blind de-identification approach via one-way encryption is an important part of our data and technology platform for personalized medicine and drug development,” said Tero Silvola, CEO of BC Platforms, a global leader in healthcare data management, analytics and access. “We work daily with highly sensitive and regulated data, and identified TripleBlind’s approach as unique and highly suited to our needs.”
TripleBlind’s Blind De-identification via one-way encryption enables data to be usable at its highest resolution without incurring an accuracy penalty. It 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 specific properties of the original data.
In addition, 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 data set in the most private way currently possible.
“When we developed TripleBlind’s mathematical breakthrough in cryptography, we were focused on the unique challenges in industries such as healthcare. Our de-identification approach for PHI and genomic data in particular means that even if one patient’s data is used in an operation by a third party, that third party lacks the means to learn anything about the individual besides the output of the operation running on the genomic sequence,” said Riddhiman Das, co-founder and CEO of TripleBlind. “Our system includes several built-in algorithms that can be used for different genomic applications, including random forest trees, lasso regression, ridge regression, neural networks and topological data analysis, alongside the ability to write custom algorithms.”
TripleBlind’s patented breakthroughs in advanced mathematics arm organizations with the ability to share, leverage and commercialize regulated data, such as PII and PHI, and mission-critical enterprise data, such as medical records. With TripleBlind, decision-makers generate new revenue for their organizations by gaining deeper insights faster, creating improved modeling and analysis, and collaborating more effectively with customers and partners and even competitors, while enabling enterprises to enforce today’s regulatory standards, such as HIPAA, GDPR and PDPA.
https://tripleblind.ai/wp-content/uploads/2021/05/BlindDe-ID@2x.png14402161TripleBlindhttps://tripleblind.ai/wp-content/uploads/2022/02/TripleBlind-Logo-H-FullColor@4x.pngTripleBlind2021-05-25 03:55:012022-03-14 12:54:26TripleBlind’s Automated and Real-Time De-Identification Facilitates Data Sharing among Healthcare Institutions, the Only Solution that De-Identifies Genomic Data
I was recently given the opportunity by insideBigData to provide a perspective on some of the possibilities present today for sharing regulated data. You can read it here as well as below where we’ve reproduced it in its entirety.
Harness the Opportunities of Sharing Regulated Data
Insights-rich but regulated or sensitive data is sitting in private data stores unleveraged and unmonetized by enterprises. In 2018, Gartner reported that nearly 97 percent of data sits unused by organizations. There are solutions available today that enable enterprises to share data and collaborate, but they are either cumbersome, slow, ineffective or dangerous – which is why the rate of data sharing remains so low. There are new solutions available that do allow enterprises to gain insights from enterprise data and address the weaknesses of current solutions, while concurrently enforcing regulatory standards such as HIPAA and GDPR, as well as data residency requirements in some regions, such as Southeast Asia, China and the Middle East.
Here are a few scenarios in which effective data collaboration would be beneficial.
On average, people own 5.3 accounts across different financial institutions. A person might have a checking and savings account with Wells Fargo, a credit card with Citibank and a mortgage with Chase. If Citibank detects potential fraud on the person’s credit card, there is currently little or no ability for Citibank’s fraud department to collaborate with Wells Fargo and Chase to get a comprehensive picture of the fraud – which would enable Citibank’s security team to identify and thwart the activity.
Approximately 1.2 billion clinical documents, such as patient records, are produced in the United States each year, comprising approximately 60% of all clinical data with each paper providing medical experts with a wealth of potentially life-saving insights and data. However, within any one healthcare system, these records are skewed by the demographics of the patients – in some parts of the country the skew might be toward older, whiter patients, in other part, younger, Hispanic patients. When these institutions develop algorithms to create diagnoses, they are impaired by this skewed data. Today, the solution is to physically ship anonymized data from other healthcare systems to create accurate algorithms, a long, slow and expensive process.
Airline Predictive Maintenance
Aircrafts supply chains are a trade secret for parts suppliers making current predictive models less accurate than they could be. Partnerships of various manufacturers are notoriously complex and often serve as a barrier in sharing data. But suppose they can privately run predictive models on the aircraft data and determine the remaining useful life of their aircrafts and parts without ever having access to the raw data sets. In that case, this can set a new precedent for the industry. The manufacturer networks will be able to share information from airlines they don’t have direct relationships with, all in compliance with local laws and protecting their intellectual property.
How One New, Breakthrough Solution Works
One new, breakthrough solution enables enterprises to gain insights from data without ever decrypting it. The process starts by privately aggregating data from multiple sources, such as different financial institutions or healthcare systems. It privately explores, selects and pre-processes relevant features for training, and then privately processes the encrypted data.
Fig. 1 Separate data from different clients are combined and privately aggregated creating a new algorithm. The data is blind to the algorithm and vice versa. The new insights are then sent back to each client without any of them ever seeing the data or algorithm themselves. The data remains encrypted throughout the entire process ensuring total security.
It then trains new, deep statistical models and then predicts on any private and sensitive data.
The training process features low compute requirements and low communication overhead.
Along with encrypted data, this new approach encrypts the algorithm. The algorithm is blind to the data fed through it and the data is blind to the algorithm executed upon it. And neither the data nor the algorithm is exposed to the solution itself – it is a triple blind answer to gain insights from sensitive data.
By incorporating algorithmic encryption, neither party can reverse engineer the algorithms and the algorithms cannot abuse the data. And, neither party can re-generate any of the original training dataset for neural networks
Compared to other approaches like homomorphic encryption or secure enclaves, this enterprise data privacy approach enables “digital rights” to the data – the ability to overlay rules on how the data may be used. This ensures that any regulation or other terms that govern the use of the data, can be baked into the digital rights management contract. This blind pipeline offers the highest privacy and security, lowest computational load, and the lowest communication overhead, with no one ever seeing the entire model. With a suite of tools that allows for even the most sensitive information to be shared among competitors, the use cases with this technology are endless. Being blind to all data and algorithms brings in the most visible results – ensuring that the data becomes “liquid” and can be used broadly.
Fig. 2 Comparison of the new blind inference solution to homomorphic encryption and secure enclaves. This compares different capabilities of each approach such as speed, digital rights management, and more.
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https://tripleblind.ai/wp-content/uploads/2021/05/SharingRegulatedData@2x.png14402161Riddhiman Dashttps://tripleblind.ai/wp-content/uploads/2022/02/TripleBlind-Logo-H-FullColor@4x.pngRiddhiman Das2021-05-18 21:07:182021-10-27 20:07:00Harness the Opportunities of Sharing Regulated Data
WHO: Riddhiman Das, Co-founder andCEO, TripleBlind Cecil Lynch, Chief Medical Information Officer, Accenture
WHAT: On May 25, Das and Lynch will discuss Accenture’s collaboration with TripleBlind and how the healthcare industry can use data sharing to speed up innovation, predict future diagnosis, and reduce complexities associated with internal and external data sharing, which often includes sensitive PII.
WHY: Private data sharing between organizations can facilitate rapid innovation in healthcare, especially by enabling AI development using previously inaccessible data. TripleBlind accelerates data sharing and collaboration and creates efficiencies by dramatically reducing arduous processes, long delays and high costs often involved with data sharing today.
WHEN: Tuesday, May 25, 2021 Strategies for Advancing Healthcare Innovation by Keeping Sensitive Data Private 12:00 pm (CT)
WHERE: Participants can register for the webinar here.
About TripleBlind TripleBlind’s patented breakthroughs in advanced mathematics arm organizations with the ability to share, leverage and monetize regulated data, such as PII and PHI, and mission-critical enterprise data, such as tax returns and banking transactions. It unlocks the estimated 105 petabytes of data stored by enterprises today that are inaccessible and unmonetized due to privacy concerns and regulations. With TripleBlind, decision-makers generate new revenue for their organizations by gaining deeper insights faster, creating improved modeling and analysis, and collaborating more effectively with customers and partners and even competitors, while enabling enterprises to enforce today’s regulatory standards, such as HIPAA, GDPR and PDPA.
https://tripleblind.ai/wp-content/uploads/2021/05/AccentureWebinar.png7201080TripleBlindhttps://tripleblind.ai/wp-content/uploads/2022/02/TripleBlind-Logo-H-FullColor@4x.pngTripleBlind2021-05-13 15:00:432021-10-27 14:39:35TripleBlind’s Riddhiman Das to Host Webinar with Accenture’s Cecil Lynch; Will Discuss Strategies for Advancing Healthcare Innovation with Private Data Sharing
Recently, we announced our partnership with BC Platforms and we’re excited to build on that momentum with our latest announcement. We officially partnered with Snowflake, the Data Cloud company, to empower joint customers to run TripleBlind’s API-driven virtual exchange solution that enables data owned by one enterprise to run specific operations on data owned by another enterprise, on Snowflake’s platform.
Snowflake’s Cloud Data Platform allows businesses or technology professionals to get the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and securely share data. It’s the ultimate solution for data warehousing, data lakes, data engineering, data science, data application development, and for securely sharing and consuming shared data. Now, when a data owner and data consumer agree to private data sharing on Snowflake’s platform, TripleBlind’s solution automatically de-identifies the data and ensures they never move outside the owner’s firewall.
The data consumer can only perform operations on the data specifically allowed by the data owner and all computations occur in the encrypted space. TripleBlind’s API-driven virtual exchange keeps intellectual property in an algorithm safe from reverse engineering attempts, while Snowflake’s secure data sharing technology means that data is never required to be moved or copied, and is up-to-date. TripleBlind’s platform is architected to natively support data sets stored in the Snowflake Data Cloud, which means customers can seamlessly integrate the solution within their instance. Read our full press release here.
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https://tripleblind.ai/wp-content/uploads/2021/05/SnowflakeCloudSecurityPrivateSharing@2x.png14402161TripleBlindhttps://tripleblind.ai/wp-content/uploads/2022/02/TripleBlind-Logo-H-FullColor@4x.pngTripleBlind2021-05-04 01:38:392022-02-16 15:05:34TripleBlind Joins the Snowflake Technology Partner Program, Supplements Innovative Cloud Security Infrastructure with Private Data Sharing
Snowflake Customers Can Safely Share Sensitive Data and Improve the Effectiveness of AML Initiatives, Healthcare Diagnoses and Finance Applications
TripleBlind Natively Supports Snowflake via JDBC, No Integration Required
KANSAS CITY, MO., May 4, 2021 – TripleBlind announced today its partnership with Snowflake, the Data Cloud company, to empower joint customers to run TripleBlind’s API-driven virtual exchange solution that enables data owned by one enterprise to run specific operations on data owned by another enterprise, on Snowflake’s platform.
“Snowflake’s platform includes multiple features such as dynamic data masking and end-to-end encryption, leveraging sophisticated cloud security technology to secure data both in transit and at rest,” said Riddhiman Das, co-founder and CEO of TripleBlind. “TripleBlind’s private data sharing builds on this strong foundation by allowing the data consumer to analyze and generate insights from sensitive data without decrypting it. With these insights, financial institutions create more effective anti-money laundering initiatives and healthcare systems that improve the quality of their diagnoses through access to larger, more diverse data sets.”
When a data owner and data consumer agree to private data sharing on Snowflake’s platform, TripleBlind’s solution automatically de-identifies the data and ensures they never move outside the owner’s firewall. The data consumer can only perform operations on the data specifically allowed by the data owner and all computations occur in the encrypted space. TripleBlind’s API-driven virtual exchange keeps intellectual property in an algorithm safe from reverse engineering attempts, while Snowflake’s secure data sharing technology means that data is never required to be moved or copied and is up-to-date. TripleBlind’s platform is architected to natively support data sets stored in the Snowflake Data Cloud, which means customers can seamlessly integrate the solution within their instance.
“Snowflake’s platform enables secure data sharing and the enforcement of privacy regulations to be seamless in the Data Cloud,” said Todd Crosslin, Global Head of Healthcare and Life Sciences, Snowflake. “Our partnership with TripleBlind can give customers the ability to securely share data in a governed way across their organizations and with partners, vendors and customers, without ever needing to move or copy the data.”
TripleBlind’s patented breakthroughs in advanced mathematics arm organizations with the ability to share, leverage and commercialize regulated data, such as PII and PHI, and mission-critical enterprise data, such as tax returns and banking transactions. It unlocks the estimated 105 petabytes of data stored by enterprises today that are inaccessible and unutilized due to privacy concerns and regulations. With TripleBlind, decision-makers generate new revenue for their organizations by gaining deeper insights faster, creating improved modeling and analysis, and collaborating more effectively with customers and partners and even competitors, while enabling enterprises to enforce today’s regulatory standards, such as HIPAA, GDPR, PDPA and other regulatory standards.
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