Researchers developed a technique guaranteeing that data remain secure during multiparty, cloud-based computation. This method, which leverages the quantum properties of light, could enable organizations like hospitals or financial companies to use deep learning to securely analyze confidential patient or customer data.
Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.
Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures. In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.
A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.
"Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks," Sulimany explains.
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