2018/11/13 by Manaar Alam, Debdeep Mukhopadhyay, Alam, Manaar +1
Computer Science · Engineering · Mathematics · #Advancements in Semiconductor Devices and Circuit Design #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Physical Unclonable Functions (PUFs) and Hardware Security #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.05259
arxiv created 2018/11/13 · openalex publication_date 2018/11/13 · arxiv updated 2018/11/14 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning system is a serious concern. There have been several efforts to analyze and exploit the information leakages from deep learning architectures to compromise data privacy. In this paper, however, we attempt to provide an evaluation strategy for such information leakages through deep neural network architectures by considering a case study on Convolutional Neural Network (CNN) based image classifier. The approach takes the aid of low-level hardware information, provided by Hardware Performance Counters (HPCs), during the execution of a CNN classifier and a simple hypothesis testing in order to produce an alarm if there exists any information leakage on the actual input.