MIT researchers have developed a groundbreaking security protocol that leverages quantum mechanics to safeguard data during cloud-based deep learning computations, paving the way for secure and accurate AI-powered applications in sensitive domains like healthcare.
Securing the Cloud for Deep Learning
Across the gamut of industries, from healthcare diagnostics to financial forecasting, deep learning models are transforming different fields. Computational demands of such state-of-the-art models frequently necessitate cloud-based servers; but this comes fraught with security risks, particularly in domains like healthcare.
To combat this, MIT researchers have now created a new security protocol that uses quantum mechanics to secure data privacy and integrity in cloud-based deep learning computations. The protocol leverages data generated by encoding the information in laser light used with traditional fiber optic communication that even when copied or intercepted undetected, provides a robust security layer for these systems without risking high-speed AI-driven applications.
Combining Quantum Mechanics and Deep Learning
What sets the protocol of these MIT researchers apart is that it builds on quantum physics fundamentals to certify the security of deep learning models and their data. The protocol to develop deep neural networks in optical field is based on encoding the weights of a deep neural network using laser light, and exploits the fact that quantum information cannot be copied perfectly (no-cloning principle).
This is because when the client performs the operations to produce a result on its private data, they also embed tiny errors into the model. The server can serialize these exceptions and verify if anything started to leak the information from client side, hence preventing such data to be leaked or stored in cyber space. At the same time, the protocol is set in such a way that it washes out any visibility the client has of the first layer of the model preventing them to learn anything else about its proprietary deep learning architecture.
Conclusion
This quantum security protocol for deep learning computations on the cloud is a major step toward achieving this goal and ensuring that AI-based applications can unlock their full potential, without exposing sensitive data or proprietary models. The act of using deep learning models to push the boundaries opens up a way to combat one of our most critical security challenges: how can we ensure that the cloud computing on which much of daily-infra relies will continue, unencumbered. The result is the first demonstration of a full-stack, AI-powered approach to ensuring data confidentiality — one that can be applied across diverse sizes and applications, from quantum sensors and medical-records to subatomic physics experiments and beyond.
