Enveil CEO Ellison Anne Williams writes about how Privacy Enhancing Technologies are enabling Privacy-Preserving Machine Learning — and how it can uniquely unlock real value for organizations today
"Perhaps unsurprisingly, one solution to ML’s trust and privacy problem comes in the form of further technology breakthroughs. An increasingly visible category known as Privacy Enhancing Technologies or PETs is delivering business-enabling capabilities for privacy-challenged use cases across verticals. When leveraged for ML applications, these technologies manifest as a specialty called Privacy-Preserving Machine Learning (PPML). PPML uses PETs to ensure that privacy is both protected and prioritized when building and utilizing models. This protection also extends to ensuring that any sensitivities regarding the data itself, including regulatory requirements or competitive advantage, are respected and protected.
PPML enables robust and reliable insight extraction to overcome many of the barriers that can limit broader business applications today. Utilizing PETs in this capacity can also uniquely facilitate decentralized frameworks for collaboration and monetization, applications that are drawing the attention of business leaders because they can lead directly to new opportunities for revenue. A decentralized approach is especially critical to achieving maximum value by broadening the potential pool of data assets and partners that can be leveraged as part of data monetization efforts.
But while establishing trust, minimizing risk, and prioritizing privacy in ML are all worthy pursuits in their own right, the value of the category will ultimately be measured by its impact, bringing us back to PPML’s unique ability to unlock real, measurable value. The overarching business drivers for PPML fall into two broad categories: rich insight extraction and new revenue generation. Organizations interested in insight extraction are using PPML to leverage third-party data for model evaluation and training. They are able to protect their interests, intentions, competitive advantage, and IP without sacrificing usability. And when it comes to uncovering new revenue streams, PPML opens the door to previously untapped data monetization opportunities by allowing other entities to leverage existing data assets for model evaluation and training. PPML protects interests, intentions, and IP while allowing data owners to retain positive control of their data assets. This is applicable in industries including financial services, healthcare, manufacturing, as well as marketing/advertising."
Read the full article at CPO Magazine.