MIT researchers have developed a groundbreaking security protocol that leverages quantum mechanics to protect sensitive data during cloud-based deep learning computations, paving the way for secure and privacy-preserving AI applications.
Safeguarding the Cloud
Cloud computing is the reality of digital and so are security threats, this holds onus true for sensitive domains such as healthcare.
Deep learning models are commonly used across a spectrum of applications — from medical diagnostics to financial forecasting, as they supersede human-level performance. Yet this running of the models — and their size — has driven organizations to the cloud, calling into question the sharing of data (healthcare patient data among other verticals), or opening up some truly proprietary model.
To combat this persistent problem, researchers at MIT have introduced a state-of-the-art security protocol that uses quantum mechanics to veil data during deep learning calculations in the cloud. The protocol, which encodes data in the laser light used for fiber optic communications, is based on fundamental laws of quantum mechanics and render interception attack virtually impossible without detection.
Unlocking the Power of Quantum
The secret of the researchers´ new technique lies in the distinctive characteristics of quantum information. Classically, it is impossible to do so due to a principle called the no-cloning theorem; quantum mechanically perfect replication of general (non-orthogonal) quantum states of qubits is also forbidden.
The researchers use this principle to develop a protocol in which the privacy of the client’s sensitive data is safeguarded as well as the security of the server’s private deep learning model. The server transmits an optical field produced by laser light that encodes the weights of the neural network to the client. Once the data has been secured it can be computed by the client without allowing the server to see it, or copy/intercept any of it.
Again, this approach based on a quantum property enables the deep learning model to keep an accuracy of around 96% and is equipped with strong security properties. Next to nothing is leaked during the client operations which makes it extremely hard for an adversary to gain anything back sensitive data or model details.
Conclusion
The advances to cloud-based deep learning, in the form of this quantum-secured security protocol from MIT researchers, represent a major step forward. They have built a solution that (with the help of quantum mechanics) tackles the essential cybersecurity and privacy issues raised by high-powered AI model use in critical domains. This breakthrough sets the stage for the deployment of practical AI solutions that respect and preserve user privacy across a variety of sectors including healthcare, personal finance, and more.
