k Nearest neighbor (kNN) classification algorithm is a prediction model which is widely used for real‐life applications, such as healthcare, finance, computer vision, personalization recommendation and precision marketing. The arrival of data explosion era results in the significant increase of feature dimension, which also makes for the increase of privacy concern over the available samples and unlabeled data in the applications of machine learning. In this paper, we present a secure low communication overhead kNN classification protocol that is able to deal with high‐dimensional features given in real numbers. First, to deal with feature values given in real numbers, we develop a specific data conversion algorithm, which is used in the chosen fully homomorphic scheme. This conversion algorithm is generic and applicable to other algorithms that need to handle real numbers using the fully homomorphic scheme. Second, we present a privacy‐preserving euclidean distance protocol (PPEDP), which works with the Euclidean distance computation between two points given in real numbers in a high‐dimensional space. Then, based on the novelty PPEDP and oblivious transfer, we propose a new classification approach, efficient secure kNN classification protocol, (ESkNN) with low communication overhead, which is appropriate for a sample set with high‐dimensional features and real number feature values. Moreover, we implement ESkNN in C++. Experimental results show that ESkNN is several orders of magnitude faster in performance than existing works, and scales up to 18 000 feature dimension in a memory limited environment.

中文翻译：

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一种具有高维特征的高效安全 k 最近邻分类协议

k 最近邻（kNN）分类算法是一种预测模型，广泛用于现实生活中的应用，例如医疗保健、金融、计算机视觉、个性化推荐和精准营销。数据爆炸时代的到来导致特征维度的显着增加，这也使得机器学习应用中对可用样本和未标记数据的隐私关注增加。在本文中，我们提出了一种安全的低通信开销 kNN 分类协议，能够处理实数中给出的高维特征。首先，为了处理以实数给出的特征值，我们开发了一种特定的数据转换算法，该算法用于所选的全同态方案。这种转换算法是通用的，适用于需要使用全同态方案处理实数的其他算法。其次，我们提出了一种保护隐私的欧几里德距离协议（PPEDP），它与高维空间中实数给定的两点之间的欧几里德距离计算一起工作。然后，基于新颖性 PPEDP 和不经意转移，我们提出了一种新的分类方法，高效的安全 kNN 分类协议（ESkNN），通信开销低，适用于具有高维特征和实数特征值的样本集。此外，我们在 C++ 中实现了 ESkNN。实验结果表明，ESkNN 在性能上比现有工作快几个数量级，并在内存有限的环境中扩展到 18 000 个特征维度。