Visible to the public PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring

TitlePReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring
Publication TypeConference Paper
Year of Publication2021
AuthorsBoulemtafes, Amine, Derhab, Abdelouahid, Ali Braham, Nassim Ait, Challal, Yacine
Conference Name2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)
KeywordsAdditives, composability, compositionality, Computational efficiency, Constrained, Deep Learning, homomorphic encryption, Human Behavior, inference, information assurance, Medical services, Metrics, Neural Network, outsourcing, privacy, pubcrawl, Random mask, resilience, Resiliency, sensitive data
AbstractHomomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.
DOI10.1109/AICCSA53542.2021.9686893
Citation Keyboulemtafes_predihero_2021