Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera
This work addresses privacy preservation in imaging for applications like surveillance or data sharing, but it is incremental as it relies on future quantum technology and is currently simulated.
The paper tackles the challenge of balancing utility and privacy in vision by proposing a quantum-based camera design that stores imagery in quantum states, and demonstrates through simulation that a control algorithm using double deep Q-learning can learn to anonymize images before measurement, achieving privacy and utility control.
Privacy-preserving vision must overcome the dual challenge of utility and privacy. Too much anonymity renders the images useless, but too little privacy does not protect sensitive data. We propose a novel design for privacy preservation, where the imagery is stored in quantum states. In the future, this will be enabled by quantum imaging cameras, and, currently, storing very low resolution imagery in quantum states is possible. Quantum state imagery has the advantage of being both private and non-private till the point of measurement. This occurs even when images are manipulated, since every quantum action is fully reversible. We propose a control algorithm, based on double deep Q-learning, to learn how to anonymize the image before measurement. After learning, the RL weights are fixed, and new attack neural networks are trained from scratch to break the system's privacy. Although all our results are in simulation, we demonstrate, with these first steps, that it is possible to control both privacy and utility in a quantum-based manner.