August 29, 2023

Help Net Security: Uncovering a privacy-preserving approach to machine learning

In this article, Enveil CEO Ellison Anne Williams discusses how privacy-preserving machine learning uniquely enables organizations to securely unlock the full potential of ML while upholding privacy.

In the era of data-driven decision making, businesses are harnessing the power of machine learning (ML) to unlock valuable insights, gain operational efficiencies, and solidify competitive advantage. Although recent developments in generative artificial intelligence (AI) have raised unprecedented awareness around the power of AI/ML, they have also illuminated the foundational need for privacy and security. Groups like IAPP, Brookings, and Gartner’s recent AI TRiSM framework have outlined key considerations for organizations looking to achieve the business outcomes uniquely available through AI without increasing their risk profile.

At the forefront of these imperatives is ML model security. Directly addressing this key area, privacy-preserving machine learning has emerged as a path to ensure that users can capitalize on the full potential of ML applications in this increasingly important field.

Machine learning models are algorithms that process data to generate meaningful insights and inform critical business decisions. What makes ML remarkable is its ability to continuously learn and improve. When a model is trained on new and disparate datasets, it becomes smarter over time, resulting in increasingly accurate and valuable insights that were previously inaccessible. These models can then be used to generate insights from data, which is referred to as model evaluation or inference.

To deliver the best outcomes, models need to learn and/or be leveraged over a variety of rich data sources. When these data sources contain sensitive or proprietary information, using them for machine learning model training or evaluation/inference raises significant privacy and security concerns. Any vulnerability of the model itself becomes a liability for the entity using it, meaning this capability that promised to deliver business-enhancing, actionable insights is now increasing the organization’s risk profile.

This issue is one of the main barriers preventing broader use of ML today. Businesses are challenged with balancing the benefits of ML with the need to protect their interests and comply with ever-evolving privacy and regulatory requirements.


Privacy-preserving machine learning uses advances in Privacy Enhancing Technologies (PETs) to address these vulnerabilities head on. PETs are a family of technologies that preserve and enhance the privacy and security of data throughout its processing lifecycle, uniquely enabling secure and private data usage. These powerful technologies allow businesses to encrypt sensitive ML models, run and/or train them, and extract valuable insights while eliminating the risk of exposure. Businesses can securely leverage disparate data sources, including across organizational boundaries and security domains, even when there are competitive interests involved.

Continue reading the full article here.

Ellison Anne Williams writes on privacy-preserving machine learning for Help Net Security
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Enveil is a pioneering Privacy Enhancing Technology company protecting Data in Use. Enveil’s business-enabling and privacy-preserving capabilities change the paradigm of how and where organizations can leverage data to unlock value. Defining the transformative category of Privacy Enhancing Technologies (PETs), Enveil’s award-winning ZeroReveal® solutions for secure data usage, sharing, collaboration, and monetization protect data while it's being used or processed. Customers can extract insights, cross-match, search, and analyze data assets at scale without ever revealing the content of the search itself, compromising the security or ownership of the underlying data, or exposing their interests and intent.
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