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Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference
Cryptography and Security · Submitted 13 May 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.
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The abstract, sentence by sentence
This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. Key switching reduces the clients bandwidth by multiple orders of magnitude, however the communication between the proxy and cloud is still excessive. Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. | contribution 1.00 | 0.83 | 0.12 | asserted 1.00 |
| s1 | Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). | background 0.76 | 0.21 | 0.02 | asserted 1.00 |
| s2 | A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. | contribution 0.83 | 0.89 | 0.07 | asserted 0.99 |
| s3 | First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. | method 0.77 | 0.93 | 0.04 | asserted 1.00 |
| s4 | Key switching reduces the clients bandwidth by multiple orders of magnitude, however the communication between the proxy and cloud is still excessive. | limitation 0.78 | 0.81 | 0.04 | asserted 1.00 |
| s5 | Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. | method 0.65 | 0.93 | 0.04 | asserted 1.00 |
| s6 | Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. | method 0.61 | 0.92 | 0.05 | asserted 0.99 |
| s7 | Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference. | result 1.00 | 0.92 | 0.79 | asserted 1.00 |
What was examined, and what was not
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The paper
Original abstract
This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. Key switching reduces the clients bandwidth by multiple orders of magnitude, however the communication between the proxy and cloud is still excessive. Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.
Human review
How the literature uses it
3 arXiv papers cite this in 14 sentences
Papers citing it · 10 sentences without a confident label
Contests it 1 paper
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Since [ref] exploit model sparsity to reduce computation, they either suffer from accuracy degradation or cannot be applied to non-sparse models.
Hyena: Optimizing Homomorphically Encrypted Convolution for Private CNN Inference · introduction · contest 0.78
Uses its method or tool 1 paper
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As in [ref] , the BFV encryption scheme [ref] is adopted in this work.
Hyena: Optimizing Homomorphically Encrypted Convolution for Private CNN Inference · Homomorphic Operations in BFV · 0.87
Cites it as background 1 paper
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In order to avoid HE-induced computational overhead, many works have exploited 2PC-based protocols [ref] to process the nonlinear activation layer.
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU · Secure Polynomial Evaluation with Existing Techniques · 0.89
What it cites · 18 sentences without a confident label
Uses the method or tool of 3 papers
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The same approach as Cheetah [ref] was used to implement the homomorphic convolution because Gazelle's implementation suffers from large computational overhead to process large CNN models in the client-cloud compute setting.
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference · Convolution Implementation Comparison · 1.00
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Note that, compared to convolution, homomorphic matrix-vector multiplication for FC layers can be implemented efficiently following [ref] , where multiple weights (e.g. rows or diagonals) are packed into a single plaintext.
Gazelle: A Low Latency Framework for Secure Neural Network Inference · Leveraging Sparsity · 0.85
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Since the performance bottleneck is mainly due to homomorphic convolution, we improve the overall latency by training a CNN with sparse kernels using prior techniques [ref] .
To prune, or not to prune: exploring the efficacy of pruning for model compression · Leveraging Sparsity · 0.95
Cites as background 3 papers
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Solutions can be categorized into two groups: HE only [ref] , or MPC-based [ref] .
DeepSecure: Scalable Provably-Secure Deep Learning · Related Work · 0.99
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Solutions can be categorized into two groups: HE only [ref] , or MPC-based [ref] .
CryptoDL: Deep Neural Networks over Encrypted Data · Related Work · 0.99
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Solutions can be categorized into two groups: HE only [ref] , or MPC-based [ref] .
Gazelle: A Low Latency Framework for Secure Neural Network Inference · Related Work · 0.99
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