  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. Secondly when updating the current classifier, private random noise
is used to protect the original data. The advantages of our algorithm are: (1)
it protects training examples better than the known classical methods; (2) it
requires no quantum database and thus is easy to implement.
