Privacy-Preserving k-NN Graphs with Autoencoder-Based Representations for Sensitive Features - Oliveira, Gustavo Lima de; Pimentel, Maria da Graça Campos; Marcacini, Ricardo M. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245212, 2024)
Privacy-preserving representation learning has gained significant attention for enabling secure data and model sharing by protecting sensitive information while maintaining data utility. In this paper, we present a new approach to privacy-preserving representation learning with k-NN-based graph models. This method maps the original feature space to a new space that balances feature utility, such as classification accuracy, with reducing privacy attack risks, and constructs a kNN graph from this new space. We evaluate three scenarios using real datasets to assess privacy-preserving graph representations. Experimental results show that learning a privacy-preserved representation and constructing a k-NN graph is a simple, intuitive, and competitive approach compared to other methods in the literature. Thus, this method enables graph data sharing with a lower risk of sensitive information extraction attacks.