Random Knn Evaluation Using Multi-Key Homomorphic Encryption

The machine learning as a service (MLaaS) concept has become increasingly popular during the past decade, as cloud platforms offer improved availability and high-performance computational resources. Nevertheless, the benefits of MLaaS come at the cost of security risks. Multi-key homomorphic encryption (MKHE) represents a solution, as it allows computations over data encrypted by multiple users under different keys. In our previous work [1], we proposed a method based on MKHE and lookup tables that allows privacy-preserving evaluations of encrypted random forest models over encrypted client queries. In this paper, we enhance the previous method, that performs only binary classification, to also allow multiclass classification. Furthermore, we demonstrate that our method can be easily adapted for the evaluation of other machine learning (ML) models and, more specifically, we provide experimental results for the Random k-Nearest Neighbors (RkNN) model. We compare the encrypted RkNN model with a traditional plaintext kNN model in terms of time efficiency and ML performance metrics. During the experiments performed on three benchmark datasets using the proposed method, we obtained classification times of the order of seconds. A minimal degradation or even improvements for the ML performance metrics were obtained in the considered scenarios.