量子隐私保护感知器
Quantum Privacy-Preserving Perceptron
摘要：
随着机器学习的广泛应用，训练样本中的隐私或敏感数据问题变得越来越严重：在训练过程中，个人信息或习惯可能会泄露给意想不到的人或组织，这可能会导致严重的隐私问题甚至经济损失。在本文中，我们提出了一个量子隐私保护算法的机器学习与感知器。保护原始训练样本主要有两个步骤。首先，在对当前分类器进行检验时，采用量子测试来检测数据使用者可能的不诚实行为。其次，在更新当前分类器时，使用私有随机噪声来保护原始数据。该算法的优点是：（1）它比已知的经典方法更好地保护训练样本;（2）它不需要量子数据库，因此易于实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
