Publishing Private High-dimensional Datasets: A Topological Approach
Narges Alipourjeddi, Ali Miri · 2022 International Wireless Communications and Mobile Computing (IWCMC) · 2022
Publishing datasets is a key part of data mining and analysis. Handling datasets containing a large number of attributes is a major challenge for analyzing these datasets. Ensuring the privacy of personal and sensitive information is also represent another main challenge. In this paper, we will show how concepts from algebraic topology, and in particular persistent homology can be used for publishing differentially private datasets. We will propose a sampling-based framework to explore the dependencies among all attributes and subsequently build a dependency graph. From the dependency graph, we will regenerate the sub-graphs privately and publish an anonymized, synthetic datasets. Evaluation results demonstrate that our method achieves superior performance when compared to other methods in the literature.