"As the amount of available data continues to grow, the need for PETs-powered capabilities will increase. Gartner sees PETs as a critical technology for the future, predicting that 'by 2025, 60 percent of large organizations will use privacy-enhancing computation techniques to protect privacy in untrusted environments or for analytics purposes'. PETs enable organizations to protect data -- and their interests -- while still ensuring its usability.
Artificial intelligence (AI) and machine learning (ML) are applications receiving a great deal of attention at the moment. While the adoption and advancement of AI and ML is certainly exciting, one area that must not be overlooked is how to leverage these capabilities securely. Privacy-Preserving Machine Learning is the fusion of Privacy Enhancing Technologies and ML. It enables organizations to extract key insights and foster collaboration, all the while safeguarding Intellectual Property, data sensitivity, and adherence to compliance benchmarks. Using PPML, organizations can center their attention on the business advantages of the outcomes obtained, rather than the inherent risks of the ML model itself and its associated utilization.
The need to balance data usage with data protection has become a keystone of modern business. With mounting regulatory pressures, rapid digital transformation and the vast quantity of data available to businesses, it is inevitable that PETs will continue to gain recognition and traction for their business-enabling characteristics. As a growing number of organizations begin to take advantage of PETs to leverage data securely and privately across silos, we will begin to see an even broader range of examples where data is being used to facilitate innovation and enhance business outcomes."
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