TripleBlind Announces $8.2 Million in Seed Funding

This week we announced the closing of $8.2 million in seed financing led by Dolby Family Ventures with strategic investment from Okta Ventures, NextGen Venture Partners, Operator Partners, Wavemaker Three-Sixty Health, AVG Basecamp Fund, Anorak Ventures, Quiet Capital, Clocktower Technology Ventures, Parity Responsible Technology Fund and Manresa Ventures. Existing investors from the first round, including Accenture Ventures, Flyover Capital and KCRise Fund exercised their pro-rata rights and participated in the round, as did several angel investors. The round was oversubscribed.

This seed round combined with the breadth and profile of investors demonstrates the widespread support for TripleBlind’s new, novel data privacy and API-driven virtual exchange approach:

Okta

“Okta is on a mission to securely enable any organization to use any technology. A key part of that is providing transparency into how data is being used. TripleBlind will enable our joint customers in regulated industries to leverage enterprise data that today goes largely untapped due to regulations such as CCPA and GDPR, and without compromising consumers’ privacy” said Monty Gray, senior vice president, Corporate Development at Okta, Inc. “TripleBlind offers synergies with our existing data privacy investments and we are excited to further our relationship to provide added benefits to our customers.”

Accenture

“Accenture Ventures invested in TripleBlind again based on the positive momentum TripleBlind has experienced and the widespread interest among fintech, healthcare and other enterprises that face data privacy challenges,” said Tom Lounibos, Managing Director of Accenture Ventures. “TripleBlind’s new round of funding so soon after its initial round in November reinforces the demand for TripleBlind data privacy solutions.”

Operator Partners

“Our team understands the challenges U.S. companies face when attempting to collaborate through sharing data in regulated industries such as financial services and healthcare,” said Nat Turner, General Partner at Operator Partners. “We are excited by TripleBlind’s ability to facilitate collaboration and enforce privacy standards within jurisdictions and in hybrid data regulation scenarios.”

Dolby Family Ventures

“With a portfolio of healthcare and biotech companies, many of which face data exchange issues, Dolby Family Ventures is enthusiastic about TripleBlind’s approach that creates and enables a more fluid data sharing process,” said Andrew Krowne, Managing Director. “We appreciate companies that build on strong, novel technical solutions and are thrilled to support TripleBlind’s potential impact across healthcare, financial services and other industries where exchange of sensitive data needs to happen.”

NextGen Venture Partners

“We have recognized the challenges with previous data privacy solutions for a long time and have been seeking out a solution with a new approach in which to invest,” said Ben Bayat, Managing Director of NextGen Venture Partners. “As a former data scientist, I appreciate the significance and uniqueness of the TripleBlind approach that puts  privacy enforcement an easy API call away, without restricting data scientists’ capability to build the right model and utilize the data to its fullest extent.”

Our announcements don’t stop here, we have more in the pipeline so be sure to keep tabs either on our website, Twitter or LinkedIn. If you’d like to learn how TripleBlind’s efficient and cost effective data privacy and clean room solution can help your business, contact us today by emailing info@tripleblind.ai or visiting https://tripleblind.ai/contact/.

Privacy Enables the Adoption of Open Banking

In this blog, we provide a more detailed exposé on the problems facing data-rich banks that are unable to leverage third party data due to regulatory and privacy concerns. It also includes more details about how TripleBlind’s solution works and unlocks new opportunities for banks and financial institutions.

The Current Problem

As countries advance towards Open Banking, or Consumer-Directed Finance, the data economy is simultaneously becoming increasingly regulated, making data privacy compliance a moving target. Strict laws and steep penalties for infractions create headaches for all parties involved in data transactions, while presenting a major hurdle in the transition to Open Banking.

Open Banking is about allowing the consumer to control where their data goes and how it is used by third party financial services (TPPs) providers to develop new products and services, opening up transparency of financial product options and encouraging collaboration between banks and financial technology firms. 

Developing the application programming interfaces (APIs) to make bank data available to consumers, financial institutions, and third-party service providers all in one place while controlled by the consumer, is just one piece of the puzzle. For Open Banking to work, data transactions must be completely private and authorized on a per-use basis.

Banks and financial institutions are hesitant to provide data even to their own partners because the risk of abuse is too high. Even with data owners (the bank or fintech) being equipped with state of the art technology, they do not have the technological means to restrict the usage of the data to legitimate uses only.

Why are Banks Reluctant to Share Data?

The bank sends the partner encrypted data so that nefarious hackers can’t snoop on the data while it’s in transit, but to do anything useful with the data, the partner institution has to decrypt it. This decryption generates a duplicate copy of the data in the raw, in the hands of the partner, which is subject to several vulnerabilities for both sides.

For the recipients/users of the data (the partners of the bank or financial institutions):

  1. Since they’re generating a copy of the data in the raw after decryption, a very stringent IT security solution has to be in place. This is quite expensive to put together. 
  2. Just having the IT security solution is not enough – certifications are also needed from third parties that the data is being handled correctly. This often costs thousands of dollars as well.
  3. Involved parties need to be compliant with all the laws and regulations that come with the data. This means that more costs will be attributed to attorneys and compliance experts.
  4. Strong stringent governance operations will also be required to ensure that it’s safe from their own employees inadvertently seeing data they’re not allowed to and abusing it.
  5. Parties may need the data just for a particular transaction, but there’s no guarantee that the recipient will delete it after it’s been used. This leaves stray data from previous uses in the recipient’s possession and stray data has been hacked in several situations in the past, including Capital One. This leads to a lot of liability issues. 
  6. Sometimes parties have to convince the sender/owner of the data that it’s not going to be abused since they’ll have decrypted data. This requires a lot of trust on both sides – which is risky to do with sensitive, regulated data.

For the senders/owners of the data (the banks or financial institutions):

  1. The default position on any data request is “no.” There’s a lot of business that isn’t happening today because no one believes data can be safely transacted. 
  2. If they choose to do business, they’ll still face big issues:
  1. Parties don’t want to just take their partner’s word that they won’t do evil. It needs to be enforced that they aren’t able to do any evil. 
  2. It’s a big headline risk. By being the victim of a data breach, reputation and trust is shattered. 
  3. There’s financial risk especially with a data breach. The partner will come with huge fines from regulators. 
  4. The partner needs to uphold their commitments to compliance with the appropriate laws and regulations, data security, and the terms of the contract. This is impossible with today’s state of the art technology, and often relies on good faith adherence to the terms of the contract.
  5. No one wants a partner that is a liability, even if the partnership is severed, the private data the partner has received in the past still holds a lot of risk.
  6. Everyone wants to avoid being the bad actor in the receiving institution, but one bad employee is all it takes. Relying on operational procedures and good-faith enforcement is not enough.

Open Banking involves providing data not only to trusted partners, but also to other fintechs and banks that may even be competitors. Because Open Banking involves sharing data with more parties, it poses additional danger to the financial data privacy of consumers, who are already wary of sharing their financial information with anyone other than their bank.

Current Workarounds

  1. Anonymized data is a common workaround to ensure that re-identification of individual customers is impossible to strip away all personally identifiable information from the dataset. The problem with this approach is that the anonymization has to be done manually, and strips away valuable information that can be useful for analysis. This manual process involves several data scientists manually cleaning, masking and hashing every attribute in every table, and can take months for just one dataset. Also, compliance is difficult – it’s been shown that even after manual anonymization, re-identification is still possible.
  2. Synthetic data involves creating a derivative dataset that does not contain any of the real data. However, this involves introducing statistical bias and noise into the dataset, and doesn’t really allow all of the real information to be extracted from the dataset. For example, outliers are often the most interesting from an analytics standpoint, and synthetic datasets can’t accommodate this.

The Solution

TripleBlind provides a Virtual Clean Room where the data can safely be used by the partners without ever exposing it to any of the risks that come with handling raw data. Powered by novel breakthroughs in advanced cryptography and mathematics, TripleBlind ensures that the financial institutions can safely work together with their partners.

TripleBlind Eliminates Decryption

TripleBlind’s Virtual Clean Room ensures that all legitimate operations on the data can be performed safely, while at the same time guaranteeing that no unauthorized operations can be performed on the data. No raw data may ever be taken out of the Virtual Clean Room. The data owner decides what operations are legitimate, and the clean room maintains an audit log of how the receiver used it every time. This Virtual Clean Room only exists for the duration of the usage of the data and vanishes after the transaction, and the real data never leaves the sender’s systems.

TripleBlind reduces the risks on both sides – banks never provide raw data, and yet, there’s no restriction around the legitimate use of the data by partners. The partners can use the data as they usually do without needing to take on the risk of working with the bank’s raw data. The real data is available to be used for the transaction, without requiring anonymization or tokenization.

For the partners, this is beneficial in several ways:

  1. Better service offering because the partners can get access to any data they need, thus building better products and services.
  2. Low liability since the partners never have raw data. They don’t present as a liability to the banks because the banks provide data in a way that only the previously agreed upon operation can be performed on it. So neither party is liable to a data abuse risk.
  3. There’s no restriction on legitimate use. TripleBlind’s novel encryption doesn’t place any restrictions on what can be done to the data with the permission of the data owner. All operations that are normally done on raw data are able to be performed on the encrypted data.
  4. Lower IT security costs  since the receiver doesn’t have to buy expensive IT security tools or go through pricey certifications processes.
  5. More accurate algorithms because they can now access more granular and more diverse data sources. Their algorithms can get more accurate and generalize better to new scenarios.

For the banks, this means:

  1. They can work with more partners to monetize their latent data assets, driving revenue straight to the bottom line.
  2. They don’t take on any additional risk in doing so.
  3. They don’t have to spin up a new data science team to spend months manually anonymizing a single dataset.
  4. They don’t have to go through the compliance, risk and review processes every time they choose to work with a partner.

For the consumer, this means:

  1. They can share their financial data however they want without risk of their data being leaked, stolen, or used for unauthorized purposes.
  2. They can benefit from better financial product and service offerings, catered to their financial needs.

Pricing

Our pricing varies so it’s best to reach out to us to receive a quote. But if you’re still not convinced by our technology, set up a demo and I’m sure we’ll change your mind. Fill out our contact form here.

How TripleBlind can Help Produce Better Models

The Power of TripleBlind Technology as demonstrated through the Blind Learning Model

It’s an age-old rule of AI: an algorithm is only as good as the data it is trained on. If the training dataset is small or biased in some way, the model will not be able to produce accurate results when new scenarios are presented. To ensure a model’s accuracy and effectiveness, it is imperative that the model be trained on a large quantity of accurate and unbiased data. However, this is often difficult to accomplish due to data scarcity. Even if an organization is able to acquire a large amount of data, this data is likely skewed in some way (location, ethnicity, gender, etc.) as it only comes from the organization’s own customers and/or users. Therefore, the organization’s models, trained only on its own data, are not universally applicable. This poses a large problem for any organization that wishes to deploy an algorithm for wide scale use. There is, however, a very clear solution: find a way to train the model across multiple datasets, provided by other organizations. 

Here, we present an example of how the TripleBlind technology can do just that: solve the problem of data scarcity while maintaining privacy.

Example with Publicly Available Data

To demonstrate the power of TripleBlind technology, we developed a test case with the following characteristics. We used portions of the MNIST* dataset held in two different organizations to train a convolutional neural network (CNN). 

After separating 29,000 MNIST images into 2 datasets of 6,000 images and 23,000 images, we handed ownership of each dataset to two different organizations, Organization A and Organization B. We first trained a CNN model using only Organization A’s dataset. The resulting model, trained with only Organization A’s 6,000 images, proved to be 91.60% accurate when tested against a separate set of 1,000 MNIST images. 

Then, using TripleBlind’s privacy ensuring technology, we used the same scripts to train a CNN model over the datasets owned by both Organization A and Organization B. Because we used TripleBlind’s platform, we were able to drastically increase the size of the training dataset, resulting in a CNN model trained over 29,000 images. We tested the model in the same way, against the same set of 1,000 MNIST images as before. The result was an accuracy of 96.10%. 

By using TripleBlind’s technology, we were able to both increase the size of the training dataset and increase the accuracy of the model. The training dataset increased from 6,000 images to 29,000 images (a 380% increase in size). This larger training dataset resulted in a model with increased accuracy- the new model was 96% accurate, as compared to the 91% accuracy of the model trained on less data.

Throughout the entirety of this process, privacy was ensured. Organization A was NOT able to see the data of Organization B. Likewise, Organization B was NOT able to see the data of Organization A. Neither organization could see the algorithm that was created. The training dataset was doubled, thus bettering the model’s accuracy, without compromising the privacy of either organization’s data. 

One should note that this example was conducted with evenly split data- the datasets were not biased in any way. In the real world, Organization A’s 6,000 images would likely be biased in some way. For this example, perhaps 90% of their 6,000 images would come from the numbers 1-5. Similarly, 90% of Organization B’s images might consist of the numbers 6-9. With these biased datasets, the accuracy differences between the first model and the model trained using TripleBlind’s platform would be much more drastic. 

*Modified National Institute of Standards and Technology

A Story of Privacy During the Pandemic

On February 27th we started something here at TripleBlind. After a conversation with Ramesh Raskar of MIT about an idea that struck him when Mike Pence’s team arrived at a healthcare conference asking for ideas of how to battle Coronavirus, we began building a contact tracing solution. (Even though we and nobody outside of certain specialists knew the term “contact tracing” back then.) This became the open source cellphone app known as Private Kit, which spawned the community behind the COVID Safe Paths app and the Safe Places system.

This system is good. It is it going to help stop the spread, it will #flattenthecurve. Even more importantly, I believe this is how we restart the world. I think having 14 days of “all green dots” is going to be the key to leaving our homes, opening our schools, rejoining our colleagues and feeling safe around strangers again.

But most importantly, this is Private.

Why is private important? It is an important sounding word, but is it really italics-worthy important? I mean, really, isn’t fighting the disease that is paralyzing the entire world more important? Can’t we forget Private for now? Let me tell you a story…

Several years ago there was a wildly contagious disease known as MERS (Middle East Respiratory Syndrome). It hit South Korea hard. Hard enough that people were scared and privacy was the last thing they cared about. A bill passed to help authorities perform contact tracing. To make it more efficient they were authorized to collect video, credit card and everyone’s cell phone location. The days preceding a patient’s diagnosis were almost perfectly reconstructed and published so the community could tell if they’d made contact with these people. It worked and MERS was stopped. So…that’s a good thing, right?

Here is the next chapter of that story. Recently, patient #15 with COVID-19 in a district of South Korea was broadcast — texted to the cellphones of everyone in a certain neighborhood. This text included a link to some facts. A female in her 20s working at Jacks Bar had been diagnosed. The trail of her last few days was published so people could tell if they’d had made contact with her. This was for the public good, a little loss of privacy is worth it — right?

Then the worst tendencies of scared people took over. The trail was examined…scrutinized by an online crowd. She’d gone home sick on March 27th. She went to eat at a restaurant on March 30th. She waited until April 3rd to go to the hospital. She even stayed with someone one night. How irresponsible! What a self-centered youth! And what about that person she’d stayed with? It was “anonymous” data, no names were ever published. But how many 20 year olds work at Jacks Bar? And how many of them live in that specific neighborhood?

People are storytellers. Points of data get connected and stories just naturally emerge. It is human nature.

Here is another version of that same story: A 20 something young woman starts to feel sick in the middle of a terrifying epidemic. ¹She leaves work early. Scared. Afraid she’s sick. Afraid she’ll lose her job. Afraid she won’t be able to pay her bills. After hiding for two days she is too weak to cook, but starving. She goes to get some food, hoping it will hold her over until it passes. She finally feels scared for her life and goes to the hospital to learn she is the victim of the virus. She was lucky and recovered. But this victim will forever be branded as the girl who jeopardized her neighbors. She’ll always be blamed for anyone who got sick, whether she actually came near them or not. The disease is gone, but she may never recover.

I’m not certain if my story is completely true. But it is why Private matters. Surveillance by a trustworthy official is easy to allow. Privacy is hard. But is it possible. And she deserved it. So does my daughter. So does my son. So do you.

This is why I care. This is why I’ve worked 18 hours a day, every single day for nearly two months. This is why TripleBlind exists.

#Covid19 #FlattenTheCurve #PrivacyMatters

 

partially inspired by segment at 7:40 of “The Coronavirus Guilt Trip

 

The Privacy “Faustian Bargain”

As many of you know I recently joined my good friend Riddihiman Das in an effort to build a cryptographically powered privacy system. We’ve been joined by a small team of experts and we are working hard to build an API that will enable bulletproof privacy as a service. Why does the world need “bulletproof privacy as a service”? I’m glad you asked! The short answer is because many of our most ubiquitous online services have developed business models that depend on surveilling us, and then “monetizing” (i.e. read “selling”) the data they accumulate. Data about us – some of which is deeply personal. 

The following was prompted by the November 20, 2019 issue of “The Download” (from the people over at MIT Technology Review). Today’s issue alone refers to at least four articles regarding the loss of privacy most of us suffer due to what the first article calls the “Faustian bargain” most users are forced to make. 

The first article, from Amnesty International is a “scathing indictment of the world’s dominant internet corporations” (https://apnews.com/7380c7d8a66443618223c5e86132cfae). The paragraph that caught my attention is “This ubiquitous surveillance has undermined the very essence of the right to privacy,” the report said, adding that the companies’ “use of algorithmic systems to create and infer detailed profiles on people interferes with our ability to shape our own identities within a private sphere.” The article then quotes Amnesty International as making a recommendation that is logical, but is unfortunately inadequate “Amnesty called on governments to legally guarantee people’s right not to be tracked by advertisers or other third parties. It called current regulations — and the companies’ own privacy-shielding measures — inadequate.” Good thought, but regulations won’t do it all. Too many legislative hurdles (i.e. read “lobbyists”), and in large parts of the world the local legal systems aren’t strong enough to enforce good regulations. At TripleBlind we think the better answer is mathematically enforced cryptography that doesn’t rely on laws, rather privacy is built into the protocol. We are working to “build in” privacy preservation, and we want to give you the keys to either lock or unlock your data as you see fit. 

The second article is about a home camera system and the many ways data (i.e. read “pictures of you, your friends and whoever walks past your house”) from these devices is used. https://arstechnica.com/tech-policy/2019/11/cops-can-keep-ring-footage-forever-share-it-with-anyone-amazon-confirms/ This article makes a couple points that caught my attention. First is the point about how the camera company shares data with other entities in ways that are not transparent to their customers (i.e. you and me). The second is the point that after the camera company shares that data with an undisclosed third party, the camera company no longer can control what happens to/with the data (i.e. read “undisclosed third party can use the pictures for whatever purpose or resale they want”). At TripleBlind we believe both of these positions are incorrect. We are working to build a system that allows you to control the release of the information in your data (note – I did not say “data”, I said “information in your data”) differentially – when, to whom, for what purpose, for what duration and at which price. 

The third article is what I think the authors meant to be a case of “surveillance for good”. I think most of us would say the goal (helping people with gambling disorders control their behavior) is a good one. https://www.bbc.com/news/technology-50486835 That said – think of the privacy implications of this application – especially when the behavior in question is coupled with a “frequent player” card/id. When the casino knows who you are, and that you display behaviors that their marketing department associates with being a “good customer” how do you think they will react? There is a very good chance the casino already knows individual customer’s gambiling behavior, and have tailored their marketing to that behavior. They are probably going to encourage you to visit the casino as often as possible. In the overall eco-system of individually targeted advertising intended to get customers in the casino – do you really think making some customers take a few second break will really make a difference? At TripleBlind we believe the better answer is to keep that “frequent player” card identity private, and allow the customer to control the dissemination of the data associated with it (and the advertising associated with it). 

Consider the fourth article a type of “public service announcement” from the folks at the Mozilla Foundation. https://foundation.mozilla.org/en/privacynotincluded/ It’s their “creepy rating” of various Christmas gift items. We can’t all go live in a cave or under a rock until better privacy tools arrive, but we can be vigilant and at least try to manage the privacy compromises we make everyday. 

In the meantime at TripleBlind we are working to deliver tools that will allow you to control your data, differentially release the knowledge in it and allow it to be interacted with algorithms in a way to protect both the data and the algorithm from disclosure. We believe this is going to be good for everyone. Once your privacy is cryptographically enforced you and the companies with which you do business will find even more interesting (and potentially lucrative) ways to use the ever larger and ever more granular data we produce every day. We might even find a way to change the terms of the privacy “Faustian bargain”.

I continue to believe this privacy thing is a big deal. 

Let’s Eat a Private Cake

After I left Ant Financial/Alibaba, I was filled with gratitude toward Ant Financial, Alibaba, and our global partners for 3 incredible years – they have been an absolute blast. No one is enabling global financial inclusion at the rate Ant Financial is, and I’m grateful to have gotten an opportunity to foster that. I worked in China, Israel, The USA, Canada, Colombia, Mexico, Brazil, India, Indonesia, Singapore, Thailand, Malaysia, the Philippines, South Korea, Hong Kong, Japan, Macau, Germany, Russia, The UK, Finland and several other countries. The work has grounded me and helped me understand how enabling global trust at the scale Ant does helps people self-actualize. I will forever be an Aliren.

As for what’s next for me – I am going to take a stab at building cryptographically powered privacy, without reliance on the legal system. This effort is called TripleBlind. We are building an API that will enable bulletproof privacy as a service, allowing the option to enforce privacy mathematically.

As more and more of our information is stored and transacted with in the digital world as opposed to the analog world, the current approaches we take to such private transactions fall short. The default approach is to slap some mumbo jumbo legalese into a privacy policy with the expectation that no one will ever read it. The evidence would suggest that these approaches don’t work – because they leave open the option to abuse the trust afforded to them by their end users.

The legal/contractual approach to privacy falls short for several reasons:

  1. It still leaves open huge holes to allow misuse of the data, intentionally or otherwise, both internally and externally. Breaking compliance requires just one incompetent or malicious actor in the entire organization. E.g. the major credit bureau using “admin/admin” as their credentials for their primary database. Or the major credit card issuer keeping all of their credit pull information in an unsecured S3 bucket.
  2. The custodians or owners of the data cannot consent to every operation performed on that data. While they might have the option to do so on paper, there’s no way to enforce it. It relies on the right organizational processes and structures in place, which are fallible, if they even exist. If the privacy policy is in the way of a particular operation, the data custodian can unilaterally change the privacy policy contract on the actual data owner. If you’re lucky, you might get an email at 3am telling you that the contract changed and you somehow already consented to it.
  3. The western world also has a tendency to take rule of law for granted. As we shift to a world where the vast majority of internet users are not from the western world, incumbent approaches that assume contracts can actually be enforced are inherently “broken”.

The core thesis around TripleBlind is that privacy enforced data & algorithm interactions can unlock the tremendous amount of value that is currently trapped in private data stores and proprietary algorithms. If we move from a world of “don’t be evil” to “can’t be evil”, we can enable entities to freely collaborate around their most sensitive data and algorithms without compromising their privacy, allowing them to work together to create compounded value in a way never before possible.

Around privacy, I believe we can have our cake and eat it too – let’s eat a private cake.