Privacy-Enhancing Technologies
A class of technologies intended to preserve privacy protection during the data usage stage, including homomorphic encryption, federated learning, differential privacy, and secure multi-party computation. Current adoption challenges include high performance overhead and significant engineering complexity.
Timeline (last 90 days)
Minando is currently collaborating with colleagues at UC Berkeley to explore deployment paths for privacy-enhancing technologies (PETs) in the mainstream world, a path that first involves working with governments and NGOs to understand data-sharing needs and barriers, then customizing or redesigning PETs, and ultimately producing a privacy deployment handbook for the mainstream world.
Privacy-enhancing technologies (PETs) include homomorphic encryption, federated learning, differential privacy and secure multi-party computation, with the goal of maintaining privacy protection at the data usage stage as well, but they still face deployment challenges such as high performance overhead and high engineering complexity.
All Facts (2)
Minando is currently collaborating with colleagues at UC Berkeley to explore deployment paths for privacy-enhancing technologies (PETs) in the mainstream world, a path that first involves working with governments and NGOs to understand data-sharing needs and barriers, then customizing or redesigning PETs, and ultimately producing a privacy deployment handbook for the mainstream world.
50%UnverifiedPrivacy-enhancing technologies (PETs) include homomorphic encryption, federated learning, differential privacy and secure multi-party computation, with the goal of maintaining privacy protection at the data usage stage as well, but they still face deployment challenges such as high performance overhead and high engineering complexity.
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