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.
@cite_6: Data analytics (such as association rule mining and decision tree mining) can discover useful statistical knowledge from a big data set. But protecting the privacy of the data provider and the data user in the process of analytics is a serious issue. Usually, the privacy of both parties cannot be fully protected simultaneously by a classical algorithm. In this paper, we present a quantum protocol for data mining that can much better protect privacy than the known classical algorithms: (1) if both the data provider and the data user are honest, the data user can know nothing about the database except the statistical results, and the data provider can get nearly no information about the results mined by the data user; (2) if the data user is dishonest and tries to disclose private information of the other, she he will be detected with a high probability; (3) if the data provider tries to disclose the privacy of the data user, she he cannot get any useful information since the data user hides his privacy among noises.
@cite_12: Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue when data mining is used to sensitive data sets like medical records, commercial data sets and national security. In this Letter, we present a quantum protocol for mining association rules on vertically partitioned databases. The quantum protocol can improve the privacy level preserved by known classical protocols and at the same time it can exponentially reduce the computational complexity and communication cost.
This paper is a continuation of our previous works @cite_12 @cite_6 . The differences between this paper and @cite_12 @cite_6 are as follows. First, the problem considered in this paper is machine learning with perceptron. In contrast, the problem dealt with in @cite_12 @cite_6 is association rule mining: @cite_12 is for semi-honest agents on vertically partitioned databases, and @cite_6 for dishonest agents on a centralized database. Second, the approaches are different. For mining association rules, quantum circuits are deployed to encode private information for semi-honest agents @cite_12 and quantum tests are employed to detect cheat of dishonest agents @cite_6 . But when learning a perceptron, classical training vectors have to be used to update the current classifier. This will cause privacy leakage. So the method in @cite_12 @cite_6 cannot be directly employed to preserve privacy in perceptron learning. Therefore, in this paper, a new quantum protocol is presented.