Quantum Privacy-Preserving Perceptron

Understand

With the extensive applications of machine learning, the issue of private or sensitive data in the training examples becomes more and more serious: during the training process, personal information or habits may be disclosed to unexpected persons or organisations, which can cause serious privacy problems or even financial loss.

• In this paper, we present a quantum privacy-preserving algorithm for machine learning with perceptron.
• There are mainly two steps to protect original training examples.
• Firstly when checking the current classifier, quantum tests are employed to detect data user's possible dishonesty.