Font size:
Print
Quantum Mechanics to Shield Data From Attackers During Cloud-Based Computation
Context:
Deep-learning models are widely applied across various fields, including healthcare diagnostics and financial forecasting. However, their high computational demands necessitate the use of powerful cloud-based servers.
Overview
- MIT researchers developed a quantum mechanics-based security protocol to protect data during cloud-based deep learning computations.
- The protocol secures both client data (e.g., patient information) and server’s proprietary model while maintaining 96% accuracy in deep learning predictions.
Key Challenge:
Cloud-based computation poses security risks, especially for sensitive fields like healthcare where confidential data is involved.
Quantum Security Protocol
- Quantum properties of light (laser light used in fibre optics) ensure that data sent between client and server remains secure.
- The no-cloning principle of quantum mechanics prevents attackers from copying or intercepting data undetected.
Process:
- Server encodes deep learning model weights into an optical field using laser light.
- The client uses the model to perform computations on private data (e.g., medical images) while ensuring data remains hidden from the server.
- The client only measures necessary light for running computations, and sends residual light back to the server for security checks.
- The server checks for tiny errors introduced by the client’s measurement to ensure no information leaks.
Security Measures
- Client cannot copy the model due to quantum limitations.
- The server detects if any data has been compromised by measuring residual errors.
- Less than 10% of information leaks about the model, and only 1% of client data is accessible to a malicious server.
Practicality
- The protocol works with existing optical fibre infrastructure, making it easily implementable without specialised hardware.
- Tested results show the protocol maintains 96% accuracy while ensuring robust security.
Future Applications
- Potential to enhance federated learning (collaborative model training across multiple parties).
- May be applied to quantum operations for further improvements in accuracy and security.
- The work is praised for combining quantum key distribution and deep learning in a unique way, offering a realistic and practical solution for securing distributed machine learning architectures.
