Researchers at MIT have developed a new security protocol aimed at enhancing the protection of data during deep-learning computations on cloud-based servers. The protocol, which utilizes the quantum properties of light, ensures secure transmission of data between clients and servers, addressing potential privacy concerns in areas such as healthcare.
As deep-learning models become more prevalent across industries, including healthcare and finance, their computational demands often require cloud-based servers. However, the use of cloud computing raises significant security issues, especially when dealing with sensitive data. In healthcare, for example, hospitals may be reluctant to use AI tools for analyzing confidential patient data due to privacy risks.
The MIT-developed protocol overcomes these concerns by encoding data into laser light used in fiber-optic communication systems, leveraging quantum mechanics to prevent interception or duplication of data. The approach guarantees security without compromising the accuracy of deep-learning models, maintaining 96 percent accuracy in initial tests.
The protocol functions within a cloud-based scenario involving two parties: a client with confidential data (e.g., medical images) and a central server hosting the deep-learning model. The client seeks to use the model to generate predictions without revealing sensitive data. At the same time, the server aims to protect the proprietary nature of its model.
The researchers’ approach takes advantage of the “no-cloning” principle in quantum information, which makes it impossible to copy data without detection. The server encodes the weights of a deep neural network into an optical field using laser light, which is then sent to the client. The client processes the data while preserving its privacy and can measure only what is necessary to run the model. Any residual light returned to the server for security checks reveals whether any information has been leaked.
The research team found that their protocol effectively prevents the leakage of data, with only a minimal amount of information potentially accessible to a malicious party. A malicious server could access just 1 percent of the necessary information to steal client data, while an attacker targeting the client would gain less than 10 percent of the required data to recover the model.
The protocol was tested using existing telecommunications infrastructure, such as optical fibers and lasers, meaning that no additional specialized hardware is needed. The research team now plans to explore further applications, including federated learning and potential use in quantum operations.
This development offers a promising step forward in ensuring the privacy and security of data in distributed machine learning systems, while maintaining the performance and accuracy of deep-learning models.
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