Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Multi-GPU design and performance evaluation of homomorphic encryption on GPU clusters

We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs. The design is put to address space and runtime requirements of FHE computations. It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols. Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing. We implement and evaluate the performance of our design on two homogeneous and heterogeneous NVIDIA GPU clusters: K80, and a customized P100. We also provide a comparison with a recent shared-memory-based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication. Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the MNIST and CIFAR - 10 datasets. Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations. In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.This work is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Programmtic Programme (Award A19E3b0099).Peer ReviewedPostprint (author's final draft

Impact of fusion gene status versus histology on riskâ stratification for rhabdomyosarcoma: Retrospective analyses of patients on UK trials

BackgroundLongâ term toxicities from current treatments are a major issue in paediatric cancer. Previous studies, including our own, have shown prognostic value for the presence of PAX3/7â FOXO1 fusion genes in rhabdomyosarcoma (RMS). It is proposed to introduce PAX3/7â FOXO1 positivity as a component of risk stratification, rather than alveolar histology, in future clinical trials.ProcedureTo assess the potential impact of this reclassification, we have determined the changes to risk category assignment of 210 histologically reviewed patients treated in the UK from previous malignant mesenchymal tumour clinical trials for nonâ metastatic RMS based on identification of PAX3/7â FOXO1 by fluorescence in situ hybridisation and/or reverse transcription PCR.ResultsUsing fusion gene positivity in the current risk stratification would reassign 7% of patients to different European Paediatric Soft Tissue Sarcoma Study Group (EpSSG) risk groups. The next European trial would have 80% power to detect differences in eventâ free survival of 15% over 10 years and 20% over 5 years in reassigned patients. This would decrease treatment for over a quarter of patients with alveolar histology tumours that lack PAX3/7â FOXO1.ConclusionsFusion gene status used in stratification may result in significant numbers of patients benefitting from lower treatmentâ associated toxicity. Prospective testing to show this reassignment maintains current survival rates is now required and is shown to be feasible based on estimated recruitment to a future EpSSG trial. Together with developing novel therapeutic strategies for patients identified as higher risk, this may ultimately improve the outcome and quality of life for patients with RMS.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137481/1/pbc26386_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137481/2/pbc26386.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137481/3/pbc26386-sup-0002-FigureS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137481/4/pbc26386-sup-0001-FigureS1.pd

A Minimax Approach for Access Point Setup Optimization in IEEE 802.11n Wireless Networks

Recently, an IEEE 802.11n access point (AP) prevailed over the wireless local area network (WLAN) due to the high-speed data transmission using the multiple input multiple output (MIMO) technology. Unfortunately, the signal propagation from the 802.11n AP is not uniform in the circumferential and height directions because of the multiple antennas for MIMO. As a result, the data transmission speed between the AP and a host could be significantly affected by their relative setup conditions. In this paper, we propose a minimax approach for optimizing the 802.11n AP setup condition in terms of the angles and the height in an indoor environment using throughput measurements. First, we detect a bottleneck host that receives the weakest signal from the AP in the field using the throughput estimation model. To explore optimal values of parameters for this model, we adopt the versatile parameter optimization tool. Then, we optimize the AP setup by changing the angles and the height while measuring throughput. For evaluations, we verify the accuracy of the model using measurement results and confirm the throughput improvements for hosts in the field by our approach

Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme

Homomorphic encryption is an emerging form of encryption that provides the ability to compute on encrypted data without ever decrypting them. Potential applications include aggregating sensitive encrypted data on a cloud environment and computing on the data in the cloud without compromising data privacy. There have been several recent advances resulting in new homomorphic encryption schemes and optimized variants. We implement and evaluate the performance of two optimized variants, namely Bajard-Eynard-Hasan-Zucca (BEHZ) and Halevi-Polyakov-Shoup (HPS), of the most promising homomorphic encryption scheme in CPU and GPU. The most interesting (and also unexpected) result of our performance evaluation is that the HPS variant in practice scales significantly better (typically by 15%-30%) with increase in multiplicative depth of the computation circuit than BEHZ, implying that the HPS variant will always outperform BEHZ for most practical applications. For the multiplicative depth of 98, our fastest GPU implementation performs homomorphic multiplication in 51 ms for 128-bit security settings, which is faster by two orders of magnitude than prior results and already practical for cloud environments supporting GPU computations. Large multiplicative depths supported by our implementations are required for applications involving deep neural networks, logistic regression learning, and other important machine learning problems

Breastfeeding and weaning practices among Hong Kong mothers: a prospective study

<p>Abstract</p> <p>Background</p> <p>Breastfeeding provides optimal and complete nutrition for newborn babies. Although new mothers in Hong Kong are increasingly choosing to breastfeed their babies, rates of exclusive breastfeeding are low and duration remains short. The purpose of this study was to describe the breastfeeding and weaning practices of Hong Kong mothers over the infant's first year of life to determine the factors associated with early cessation.</p> <p>Methods</p> <p>A cohort of 1417 mother-infant pairs was recruited from the obstetric units of four public hospitals in Hong Kong in the immediate post-partum period and followed prospectively for 12 months or until weaned. We used descriptive statistics to describe breastfeeding and weaning practices and multiple logistic regression to investigate the relationship between maternal characteristics and breastfeeding cessation.</p> <p>Results</p> <p>At 1 month, 3 months, 6 months and 12 months only 63%, 37.3%, 26.9%, and 12.5% of the infants respectively, were still receiving any breast milk; approximately one-half of breastfeeding mothers were exclusively breastfeeding. Younger mothers, those with a longer duration of residence in Hong Kong, and those returning to work postpartum were more likely to wean before 1 month. Mothers with higher education, previous breastfeeding experience, who were breastfed themselves and those who were planning to exclusively breastfeed and whose husbands preferred breastfeeding were more likely to continue breastfeeding beyond 1 month. The introduction of infant formula before 1 month and returning to work postpartum were predictive of weaning before 3 months.</p> <p>Conclusions</p> <p>Breastfeeding promotion programs have been successful in achieving high rates of breastfeeding initiation but the focus must now shift to helping new mothers exclusively breastfeed and sustain breastfeeding for longer.</p