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

The genotype and allele frequencies of HIF1A gene P582S and A588T polymorphisms in controls from Caucasian and Asian groups.

*<p>Study by Nadaoka J was not included;</p>a<p><i>P</i><0.05 for the comparison between HIF1A gene P582S genotypes;</p>b<p><i>P</i> value for the dominant models;</p>c<p><i>P</i><0.05 for the allele models.</p

Forest plot of HIF1A gene P582S polymorphism and the risk of urinary cancers in the recessive, dominant and allele models.

<p>Forest plot of HIF1A gene P582S polymorphism and the risk of urinary cancers in the recessive, dominant and allele models.</p

The flow diagram of search strategy in this meta-analysis.

<p>The flow diagram of search strategy in this meta-analysis.</p

DataSheet1_Bactericidal and biofilm eradication efficacy of a fluorinated benzimidazole derivative, TFBZ, against methicillin-resistant Staphylococcus aureus.docx

Methicillin-resistant Staphylococcus aureus (MRSA) is a major inducement of nosocomial infections and its biofilm formation render the high tolerance to conventional antibiotics, which highlights the requirement to develop new antimicrobial agents urgently. In this study, we identified a fluorinated benzimidazole derivative, TFBZ, with potent antibacterial efficacy toward planktonic MRSA (MIC = 4 μg/mL, MBC = 8 μg/mL) and its persistent biofilms (≥99%, MBEC = 8 μg/mL). TFBZ manifested significant irreversible time-dependent killing against MRSA as characterized by diminished cell viability, bacterial morphological change and protein leakage. Furthermore, the results from CBD devices, crystal violet assay in conjunction with live/dead staining and scanning electron microscopy confirmed that TFBZ was capable of eradicating preformed MRSA biofilms with high efficiency. Simultaneously, TFBZ reduced the bacterial invasiveness and exerted negligible hemolysis and cytotoxicity toward mammalian cells, which ensuring the robust therapeutic effect on mouse skin abscess model. The transcriptome profiling and quantitative RT-PCR revealed that a set of encoding genes associated with cell adhesion, biofilm formation, translation process, cell wall biosynthesis was consistently downregulated in MRSA biofilms upon exposure to TFBZ. In conclusion, TFBZ holds promise as a valuable candidate for therapeutic applications against MRSA chronic infections.</p

Main results of meta-analysis for the association of HIF1A gene P582S polymorphism and urinary cancers risk.

<p>HWE: Hardy-Weinberg Equilibrium.</p

Main results of meta-analysis for the association of HIF1A gene A588T polymorphism and urinary cancers risk.

<p>HWE: Hardy-Weinberg Equilibrium.</p

Forest plot of HIF1A gene A588T polymorphism and the risk of urinary cancers in the dominant and allele models.

<p>Forest plot of HIF1A gene A588T polymorphism and the risk of urinary cancers in the dominant and allele models.</p

Results of Begg’s test for HIF1A gene C1772T (A) and G1790A (B) polymorphisms in the dominant model.

<p>Results of Begg’s test for HIF1A gene C1772T (A) and G1790A (B) polymorphisms in the dominant model.</p

DataSheet_1_Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.zip

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.</p