Analyzing, searching, and performing calculations on encrypted data hasn’t historically been possible at scale. But thanks to the emerging technique of homomorphic encryption, it’s becoming feasible at the production level. One of the firms at the forefront is Enveil, which was founded by Ellison Anne Williams, a former National Security Agency senior researcher and previously a senior scientist at the Johns Hopkins University of Applied Physics Laboratory. Enveil has kept a low profile for the better part of three years, but this morning it announced that it’s raised $10 million in series A funding, bringing its total raised to approximately $15 million.
Williams said that the tranche — which was led by C5 Capital with participation from Mastercard, Capital One Ventures, Bloomberg Beta, and 1843 Capital — will enable Enveil to expand its product line as it collaborates with strategic backers including DataTribe, Bloomberg Beta, Thompson Reuters, the USAA, CIA-linked In-Q-Tel, and Refinitiv. She added that it will also help the company cultivate traction within commercial and government markets, grow its customer support functions, and expand its sales team.
“[W]e’ve successfully created a market, solidified customer use cases, executed enterprise deployments, and expanded our capabilities for protecting data in use where it is and as it is today,” Williams said. “We are privileged to be joined by this strong team of investors who recognize both our leading technical capabilities and the converging, cross-functional data protection requirements in this category.”
Homomorphic encryption isn’t new — IBM researcher Craig Gentry developed the first scheme in 2009 — but it’s gained traction in recent years, coinciding with advances in compute power and efficiency. It’s basically a form of cryptography that enables computation on plaintext (file contents) encrypted using an algorithm (also known as ciphertexts), so that the generated encrypted result exactly matches the result of operations that would have been performed on unencrypted text. Using this technique, a “cryptonet” (e.g, any learned neural network that can be applied to encrypted data) can perform computation on data and return the encrypted result back to some client, which can then use the encryption key — which was never shared publicly — to decrypt the returned data and get the actual result.
In practice, homomorphic encryption libraries don’t yet fully leverage modern hardware, and they’re at least an order of magnitude slower than conventional models. But newer projects like cuHE, an accelerated encryption library, claim speedups of 12 to 50 times on various encrypted tasks over previous implementations. Moreover, libraries like PySyft and tf-encrypted — which are built on Facebook’s PyTorch machine learning framework and TensorFlow, respectively — have made great strides in recent months. So, too, have abstraction layers like HE-Transformer, a backend for nGraph (Intel’s neural network compiler) that delivers leading performance on some cryptonets.
Enveil’s API-based product — ZeroReveal — sits above sensitive data, requiring no changes to the underlying compute environment. It works alongside existing services, complementing data-at-rest and data-in-transit encryption technologies, while at the same time delivering a flavor of homomorphic encryption that’s led to 13 patent applications to date.
ZeroReveal is a two-party setup consisting of the ZeroReveal client app, which lives within an enterprise client’s network, and the ZeroReveal server app, which is deployed where data resides. The apps in tandem function as a point-to-point proxy, ensuring the content of interactions like on-premises and cloud analytics are always protected regardless of system architecture, data storage format, or app code.
Enthusiasm for homomorphic encryption has given rise to a cottage industry of startups estimated to be worth a combined $268.3 million by 2027. Newark, New Jersey-based Duality Technologies, which recently attracted funding from one of Intel’s venture capital arms, pitches its homomorphic encryption platform as a privacy-preserving solution for “numerous” enterprises, particularly those in regulated industries. And in March 2019, Paris-based Cosmian raised €1.4 million (about $1.5 million) for a data encryption product that combines functional and homomorphic encryption.