December 6, 2023

Exploring Privacy Enhancing Technologies? 4 Key Considerations

Data-driven businesses need Privacy Enhancing Technologies. In this article, we outline why PETs are transformational.

Privacy Enhancing Technologies (PETs) is a category known for its ability to enable and preserve the security and privacy of data when it is being used or processed. PETs allow data to be used in a secure and private capacity, enabling data assets to be used to unlock value in ways that were not previously possible, and they are already having an impact today for use cases relating to cross-silo data collaboration, third party data usage, and Secure AI.

Here are four key considerations for data-driven businesses exploring the use of Privacy Enhancing Technologies:

1) PETs are business enabling. Both the amount of data available and organizational necessity to leverage that data to extract value will continue to grow. The need for these PETs-powered capabilities has never been greater and will only continue to increase: Gartner analysts predict that “by 2025, 60% of large organizations will use privacy-enhancing computation techniques to protect privacy in untrusted environments or for analytics purposes."

2) PETs uniquely foster secure and private data usage. The ability to overcome data silos and barriers to leverage data securely and privately changes the game. Organizations can protect data — and their interests — while still ensuring its usability. It is critical to continue to educate the market on the power of PETs to ensure data is protected throughout its processing lifecycle.

3) Standardization and regulatory actions are catalysts to the adoption of PETs. PETs are ready and are being adopted today; there are solid examples of PETs being used at scale to solve business and mission challenges. While the capabilities PETs enable are transformational and thus organizations are moving forward with their use, wide-scale adoption would be accelerated if standardization bodies provided some broad implementation guidance. Further, if regulated organizations are incentivized by the regulator to put PETs to use in operational settings, such as for financial crime detection in banking, it will have a substantial impact on broader adoption for regulated use cases.

4) PETs can enable Secure AI. Organizations everywhere are looking for ways to implement AI/ML without compromising security, and that is made possible through Privacy-Preserving Machine Learning (PETs + ML). Privacy-Preserving ML provides an innovative path to extracting critical insights and driving collaboration AI/ML efforts while preserving both IP and necessary data sensitivity requirements and compliance standards. PETs contribute to the broader ML landscape in two substantial ways: by protecting models during evaluation (sometimes called inference) and training, allowing an organization’s focus to remain on the business benefits of the results derived rather than the risks inherent in the ML model itself and its surrounding activity.

The need to securely and privately leverage data is not a passing trend. Whether led by market demand or regulation, organizations must be ready to operate at a global scale in a world that prioritizes data protection and privacy. PETs uniquely deliver solutions to this challenge. 

Learn more via this Datanami article by Enveil CEO Ellison Anne Williams or explore our ZeroReveal solutions.

To learn more about the expanded value unlocked by Enveil, please schedule a meeting.
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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