Computer Science > Cryptography and Security
[Submitted on 6 May 2022 (v1), last revised 10 Jun 2023 (this version, v5)]
Title:Fusion: Efficient and Secure Inference Resilient to Malicious Servers
View PDFAbstract:In secure machine learning inference, most of the schemes assume that the server is semi-honest (honestly following the protocol but attempting to infer additional information). However, the server may be malicious (e.g., using a low-quality model or deviating from the protocol) in the real world. Although a few studies have considered a malicious server that deviates from the protocol, they ignore the verification of model accuracy (where the malicious server uses a low-quality model) meanwhile preserving the privacy of both the server's model and the client's inputs. To address these issues, we propose \textit{Fusion}, where the client mixes the public samples (which have known query results) with their own samples to be queried as the inputs of multi-party computation to jointly perform the secure inference. Since a server that uses a low-quality model or deviates from the protocol can only produce results that can be easily identified by the client, \textit{Fusion} forces the server to behave honestly, thereby addressing all those aforementioned issues without leveraging expensive cryptographic techniques. Our evaluation indicates that \textit{Fusion} is 48.06$\times$ faster and uses 30.90$\times$ less communication than the existing maliciously secure inference protocol (which currently does not support the verification of the model accuracy). In addition, to show the scalability, we conduct ImageNet-scale inference on the practical ResNet50 model and it costs 8.678 minutes and 10.117 GiB of communication in a WAN setting, which is 1.18$\times$ faster and has 2.64$\times$ less communication than those of the semi-honest protocol.
Submission history
From: Caiqin Dong [view email][v1] Fri, 6 May 2022 06:42:48 UTC (319 KB)
[v2] Thu, 2 Jun 2022 01:50:52 UTC (1 KB) (withdrawn)
[v3] Tue, 7 Jun 2022 02:03:48 UTC (622 KB)
[v4] Sun, 30 Oct 2022 04:33:37 UTC (1,336 KB)
[v5] Sat, 10 Jun 2023 17:25:58 UTC (499 KB)
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