Computer Science > Machine Learning
arXiv:2210.15614 (cs)
[Submitted on 27 Oct 2022]
Title:Private and Reliable Neural Network Inference
View a PDF of the paper titled Private and Reliable Neural Network Inference, by Nikola Jovanovi\'c and 3 other authors
View PDFAbstract:Reliable neural networks (NNs) provide important inference-time reliability guarantees such as fairness and robustness. Complementarily, privacy-preserving NN inference protects the privacy of client data. So far these two emerging areas have been largely disconnected, yet their combination will be increasingly important. In this work, we present the first system which enables privacy-preserving inference on reliable NNs. Our key idea is to design efficient fully homomorphic encryption (FHE) counterparts for the core algorithmic building blocks of randomized smoothing, a state-of-the-art technique for obtaining reliable models. The lack of required control flow in FHE makes this a demanding task, as naïve solutions lead to unacceptable runtime. We employ these building blocks to enable privacy-preserving NN inference with robustness and fairness guarantees in a system called Phoenix. Experimentally, we demonstrate that Phoenix achieves its goals without incurring prohibitive latencies. To our knowledge, this is the first work which bridges the areas of client data privacy and reliability guarantees for NNs.
| Comments: | In ACM Conference on Computer and Communications Security (CCS 2022) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2210.15614 [cs.LG] |
| (orarXiv:2210.15614v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2210.15614 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Private and Reliable Neural Network Inference, by Nikola Jovanovi\'c and 3 other authors
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