Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

Differentially expressed proteins and their functional classification analysis during artificial ageing.

<p>(A) Differentially expressed proteins during artificial ageing compared with unaged seeds; (B) sub-cellular localization analysis; (C) eukaryotic orthologous group (KOG) analysis.</p

Quantitative Proteomic Analysis of Wheat Seeds during Artificial Ageing and Priming Using the Isobaric Tandem Mass Tag Labeling

<div><p>Wheat (<i>Triticum aestivum</i> L.) is an important crop worldwide. The physiological deterioration of seeds during storage and seed priming is closely associated with germination, and thus contributes to plant growth and subsequent grain yields. In this study, wheat seeds during different stages of artificial ageing (45°C; 50% relative humidity; 98%, 50%, 20%, and 1% Germination rates) and priming (hydro-priming treatment) were subjected to proteomics analysis through a proteomic approach based on the isobaric tandem mass tag labeling. A total of 162 differentially expressed proteins (DEPs) mainly involved in metabolism, energy supply, and defense/stress responses, were identified during artificial ageing and thus validated previous physiological and biochemical studies. These DEPs indicated that the inability to protect against ageing leads to the incremental decomposition of the stored substance, impairment of metabolism and energy supply, and ultimately resulted in seed deterioration. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the up-regulated proteins involved in seed ageing were mainly enriched in ribosome, whereas the down-regulated proteins were mainly accumulated in energy supply (starch and sucrose metabolism) and stress defense (ascorbate and aldarate metabolism). Proteins, including hemoglobin 1, oleosin, agglutinin, and non-specific lipid-transfer proteins, were first identified in aged seeds and might be regarded as new markers of seed deterioration. Of the identified proteins, 531 DEPs were recognized during seed priming compared with unprimed seeds. In contrast to the up-regulated DEPs in seed ageing, several up-regulated DEPs in priming were involved in energy supply (tricarboxylic acid cycle, glycolysis, and fatty acid oxidation), anabolism (amino acids, and fatty acid synthesis), and cell growth/division. KEGG and protein-protein interaction analysis indicated that the up-regulated proteins in seed priming were mainly enriched in amino acid synthesis, stress defense (plant-pathogen interactions, and ascorbate and aldarate metabolism), and energy supply (oxidative phosphorylation and carbon metabolism). Therefore, DEPs associated with seed ageing and priming can be used to characterize seed vigor and optimize germination enhancement treatments. This work reveals new proteomic insights into protein changes that occur during seed deterioration and priming.</p></div

Comparisons of the protein and mRNA expression patterns of three representative DEPs at four artificial ageing stages (WH98, WH50, WH20, and WH01) by iTRAQ and qRT-PCR.

<p>Solid lines represent mRNA expression patterns, and dotted lines represent protein expression patterns.</p

The protein-protein interaction network analysis of up-regulated proteins (A) and down-regulated proteins (B) identified by TMT-labeling during seed priming.

<p>The protein-protein interaction network analysis of up-regulated proteins (A) and down-regulated proteins (B) identified by TMT-labeling during seed priming.</p

Functional enrichment-based clustering of protein groups during artificial ageing.

<p>(A) Biological process; (B) Cellular component; (C) Molecular function; (D) KEGG pathway; (D) Protein domain.</p

Wheat seed development during the artificial ageing of cultivar ‘Aikang58’.

<p>(A) Seed germination during artificial ageing. (B) Scanning electronic microscope observations of embryos from artificial ageing grains.</p

Functional enrichment analysis of DEPs during seed priming.

<p>The red bar represents up-regulated proteins, and the green bar represents down-regulated proteins. (A, B) GO enrichment analysis; (C) KEGG enrichment analysis; (D) Protein domain enrichment analysis.</p

The curative effect of the HRS prescription and the Visual changes in pathological anatomy in each group.

<p>(I) The curative effect of the HRS prescription. ^a-c Bars in the same index without the same superscripts differ significantly (<i>p</i> < 0.05). (II) Visual changes in pathological anatomy in the HRS (A), VC (B) and BC (C) groups. Arrows a and c: hemorrhagic spots; arrow b: ecchymosis.</p

Relative DHAV gene expression levels in each group.

<p>Viral gene expression level in the VC group at 4 hpi was set to 1. ^a-c Bars at the same time point without the same superscripts differ significantly (<i>p</i> < 0.05). hpi: hours post-injection.</p