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Enveil Announces New Machine Learning Capabilities to Unlock Data Value through Encrypted Training

Product leverages Privacy Enhancing Technologies to drive value by enabling the secure usage of cross-silo data sources.

Enveil, the pioneering Privacy Enhancing Technology company protecting Data in Use, announced the release of its new encrypted training solution, ZeroReveal ML Encrypted Training (ZMET). The enterprise-ready product extends the boundary of trusted compute by enabling encrypted federated learning and the secure usage of disparate, decentralized datasets for machine learning applications. Designed to address specific customer pain points, ZMET allows organizations to train models in an encrypted capacity while ensuring the model development process, the model itself, and the interests to all parties involved remain protected. The product expansion, an extension of Enveil’s machine learning solution suite, comes on the heels of the company’s $25 million Series B funding announcement.

The rise of the digital economy is driving a broad market need to span global data silos and extract insights through secure and private data usage, analytics, and machine learning. Enveil’s ZeroReveal solutions empower this digital transformation by changing the paradigm of how and where organizations can leverage data to unlock value. A recent Gartner “Innovation Insight for Federated Machine Learning” report, which recognizes Enveil as a Representative Provider, highlights this market momentum: “By 2025, 80% of the largest global organizations will have participated at least once in federated machine learning (FedML) to create more accurate, secure and environmentally sustainable models” (March 2022).

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“Today’s digital-first business landscape demands solutions that expand an organization’s reach without sacrificing privacy or security,” said Dr. Ellison Anne Williams, Founder and CEO of Enveil. “By ensuring models are securely trained – and that the model itself and its associated results remain encrypted – ZeroReveal Machine Learning allows organizations to leverage ML to securely derive insights from data sources across silos, jurisdictions, or boundaries, even when using highly sensitive models or training data.”

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ZMET utilizes advances in Privacy Enhancing Technologies, namely Secure Multiparty Computation (SMPC), for training models in an encrypted capacity. This encrypted training process enables secure federated learning, protecting the model development process, the data used for training, as well as the interests and intent of the parties involved. Organizations can confidently leverage sensitive data and/or ML models during training without risk of exposure, delivering enhanced models that can more accurately be used to derive insights and deliver value. Models can be trained using data sources across security domains and organizational boundaries without the risk of unintended exposure.

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“We are proud to be the first in our category to deliver an encrypted training product with a concrete and verifiable security posture: ZMET is delivering an unmatched ability to derive insights from data without the need to trust other parties during computation,” said Dr. Ryan Carr, Chief Technology Officer at Enveil. “These privacy-preserving machine learning training capabilities are grounded in the needs of our customers, engineered to overcome obstacles and add business and mission value for ML and data science use cases today.”

At its core, ZeroReveal Machine Learning is a two-party, proxy layer software system enabling decentralized, distributed evaluation and training of encrypted machine learning models across multiple datasets. Enveil protects the content of the search, analytic, or machine learning model – and its corresponding results. The company’s decentralized approach allows data to be securely leveraged between entities and across organizational, jurisdictional, and security boundaries, expanding data utility without the need to move or pool sensitive assets.

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[To share your insights with us, please write to sghosh@martechseries.com]

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