Quantum Decision-Making: Neural Network Privacy

This lecture discusses the quantum decision-making model and its application in recognizing individuals through image processing. The instructor explains how to create templates for various properties of individuals by taking multiple pictures. The process involves dividing the model to verify identities, such as confirming that a person in a database matches the images provided. The lecture also covers the use of networks to compute gradients of synthetic data, comparing these with the gradients received by the neural network. The concept of privacy in neural networks is introduced, emphasizing that the network remains private unless certain probabilistic events occur. The instructor highlights the importance of selecting appropriate variables for noise in gradients, which can affect the model's privacy. The discussion concludes with the implications of these parameters on the security of the neural network against potential attackers, illustrating the balance between data integrity and privacy in quantum decision-making frameworks.