Acies: A Privacy-Preserving System for Edge-Based Classification
Wanli Xue, Yiran Shen, Chengwen Luo, Wen Song Hu, Aruna Prasad Seneviratne · 2018
In this paper, we propose Acies, a differential privacy based privacy-preserving classification system for edge computing to secure the classification models offloaded to edge devices. Acies supports popular classifiers such as Nearest Neighborhood, Support Vector Machine and Sparse Representation Classifier with a variety of feature selection methods. According to our evaluation on different datasets, classification models with Acies can be private and remain high utility. Acies achieves reliable privacy protection under reconstruction attacks with minimal impact on classification accuracy (2%-5%) only. Acies outperforms the naive input dataset perturbation methods by up to 30% higher classification accuracy when the privacy requirements of the applications is high (ϵ is less than 2).