Research tests machine learning models for cloud security.
problem Lack of real-time, unbiased datasets for machine learning in cloud security.
method Used UNSW and ISOT datasets to train and test supervised machine learning models.
result Machine learning models trained on diverse datasets show robustness in cloud security.
Paper proposes DPN for encrypted speech recognition, maintaining privacy and security.
problem Privacy and security issues in cloud-based speech recognition.
method Deep Polynomial Network (DPN) for encrypted speech, cloud-local joint decoding.
result DPN can make frame-level predictions over encrypted speech and return them in encrypted form.
Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle…
Secure submodel learning protects privacy in federated learning.
problem Efficiency and privacy in federated learning for resource-constrained clients.
method Designing a secure federated submodel learning scheme with randomized response, secure aggregation, and Bloom filter.
result Demonstrated the feasibility and scalability of the scheme with practical evaluations.
Privacy-preserving distributed deep learning method for multiple classification.
problem Privacy issues in training deep learning models for various fields.
method Split learning into common extractor, cloud model, and local classifier.
result Average performance improvement of 2.63% over existing local training models.
SANNS secures k-NNS queries and results without revealing them.
problem Securely searching for nearest neighbors without exposing sensitive data.
method Optimized linear scan and clustering-based algorithm with cryptographic primitives.
result SANNS achieves up to 31x faster response times and scales to 10 million entries.
CHEETAH speeds up secure MLaaS by 100x over fastest existing schemes.
problem Privacy-preserving machine learning on end devices.
method Ultra-fast secure MLaaS framework using secret sharing.
result More than 100x speedup over fastest existing schemes.
This paper improves federated learning efficiency by auto-tuning secure aggregation parameters.
problem Efficient communication in federated learning with quantized updates.
method Auto-tuning secure aggregation parameters based on random rotation properties.
result Improved communication efficiency in federated learning with secure aggregation.
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.
problem Protecting LLMs on edge devices from theft and unauthorized use.
method SLIP uses matrix decomposition to split model computation between secure and vulnerable resources, ensuring zero accuracy degradation and minimal latency.
result SLIP is the first practical, secure hybrid protocol for protecting LLMs on edge devices.
A new federated learning method protects privacy in mobile crowdsensing.
problem Data and model privacy protection in federated extreme gradient boosting for mobile crowdsensing.
method Secret sharing based federated learning architecture FedXGB.
result FedXGB achieves less than 1% accuracy loss while preserving model privacy.
Secure asynchronous federated learning with differential privacy for edge intelligence.
problem Privacy concerns in federated learning for edge computing.
method Differential privacy applied to secure asynchronous federated learning.
result MAPA improves model accuracy and convergence speed with sufficient privacy.
FlexServe simplifies deployment of PyTorch models as REST endpoints.
problem Lack of control over model evolution and strict security requirements in operational environments.
method Developed FlexServe, a library to deploy multi-model ensembles with flexible batching.
result Rapid deployment of PyTorch models without intermediate transformations.
New method stabilizes inputs to DNN for secure inference with LHE.
problem Incompatibility of LHE with nonlinear functions in DNN.
method Training with polynomial approximations and Min-Max normalization.
result Loss in prediction accuracy reduced to small values or eliminated.
This paper explores advancements in neural network communication for distributed settings.
problem Challenges in training and evaluating neural networks in distributed, privacy-constrained environments.
method Development and evaluation of novel training and evaluation schemes for neural networks.
result Advantages of novel approaches over traditional cloud solutions, including privacy-preservation, increased security, and high training speed.
Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.
problem Optimizing systemic risks in Compound V3 protocol.
method Millions of Chainrisk simulations to evaluate VaR and LaR, providing parameter adjustments.
result Optimization framework enhances protocol stability.
DeepQuarantine detects and quarantines suspicious emails.
problem High-quality spam detection and prevention.
method Convolutional Neural Networks on MIME headers for deep feature extraction.
result DQ enhances spam detection with high precision.
Edge language models show bias over time, especially on resource-constrained devices.
problem Bias in edge language models on resource-constrained devices.
method Comparative analysis of text-based bias across edge, cloud, and desktop environments; optimized Llama-2 model on Raspberry Pi 4; feedback loop mechanism to correct bias.
result Llama-2 on Raspberry Pi 4 shows 43.23% and 21.89% more bias over time compared to cloud and desktop models.
Model extraction attacks exploit MLaaS query interfaces to steal models.
problem Model extraction attacks compromise MLaaS systems by stealing models through query interfaces.
method Formalized model extraction, compared it to active learning, and investigated defense strategies.
result Recent active learning advancements can be used to implement powerful model extraction attacks.
DeepPeep attacks DNN architectures to reveal design details, posing IP theft risks.
problem Protecting DNN architecture from IP theft in cloud-based services.
method Two-stage attack methodology exploiting design characteristics.
result DeepPeep successfully reverses-engineers compact DNN architectures.
FTL improves model training across data silos but requires optimization.
problem Data privacy and security concerns in federated learning.
method Quantitative analysis of FATE on Google Cloud, identifying bottlenecks.
result Inter-process communication, data encryption, and networking condition are major performance issues.
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields. Recently, Pauwels and Lasserre (2016) noticed that the sublevel sets of the inverse Christoffel function accurately depict the shape of a cloud of data …
STORM enables edge computing for empirical risk minimization.
problem Training models on edge devices for streaming data.
method Online sketching for empirical risk minimization.
result STORM can estimate least-squares objective accurately.
While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, wit…
Generative model for 3D point clouds using invertible flows.
problem Generating realistic 3D point clouds.
method Invertible flow-based models for point cloud generation with parameter sharing and embedding vectors.
result The model generates high-quality 3D point clouds with good similarity.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotati…
New method upsamples sparse, non-uniform point clouds more accurately.
problem Suboptimal results from existing point cloud upsampling methods.
method Imposes manifold distribution constraints using Gaussian functions.
result Generates higher-quality, more uniformly distributed dense point clouds.
Isthmus platform simplifies ML/AI integration in healthcare.
problem Challenges in deploying ML models in healthcare due to data quality, regulatory, and security issues.
method Turnkey, cloud-based platform addressing data quality, clinical relevance, and regulatory compliance.
result Reduces time to market for operationalizing ML/AI in healthcare.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…
Framework optimizes cloud container sizing for ML tasks.
problem Challenges in cloud container configuration for ML services.
method Autonomous scaling using nested-loop Monte Carlo simulation.
result Reduces compute cost and accelerates ML algorithms.
Convolutional LSTMs classify clouds as noise, improving remote sensing accuracy.
problem Clouds hinder remote sensing accuracy; current methods are inadequate.
method Used a Convolutional LSTM network to classify clouds as noise.
result Network internalizes cloud-filtering mechanism without explicit training.
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
Self-supervised method learns from unlabelled point clouds by reconstructing them.
problem Efficiently learning from large, unlabelled 3D point cloud datasets.
method Trains neural networks to reconstruct point clouds with randomly rearranged parts.
result Method learns semantic properties of point clouds and improves downstream object classification.
A novel method compares 3D point clouds using information geometry.
problem Comparing 3D point clouds in machine learning applications.
method Interprets point clouds as probability density functions on a statistical manifold, using GMM and Modified Symmetric KL divergence.
result Demonstrates effectiveness through various case studies.
Deep learning detects cloud changes due to human aerosols.
problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.
Develops a method for conformal parameterization of point clouds without fixed boundaries.
problem Desirable distortion in fixed-boundary parameterizations of point clouds.
method Free-boundary conformal parameterization method involving approximation of point cloud Laplacian and boundary treatment.
result High-quality point cloud meshing achieved through the proposed method.
Algorithm classifies point clouds using deep set linearized optimal transport.
problem Classifying point clouds efficiently and accurately.
method Deep Set Linearized Optimal Transport, ICNNs, and a discriminator network.
result Efficiently distinguishes between various classes of point clouds.
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task,…
Cumulo dataset for cloud classification at 1km resolution.
problem Uncertainty in cloud modelling for climate projections.
method Benchmark dataset of 1km MODIS imagery and CloudSat labels.
result IResNet model discovers new cloud sub-classes.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.
problem Identifying the boundary of a domain from point cloud samples.
method Developed new estimators for normal vectors, distances, and boundary tests; provided error estimates.
result Efficient and accurate estimators for boundary properties on point clouds.
The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are of…
The article discusses how to create a special type of triangle mesh for surfaces in 3D space.
problem Creating a special type of triangle mesh for surfaces in 3D space.
method Using sufficient conditions and the diagonal switch algorithm to find an embedded Delaunay triangulation.
result The diagonal switch algorithm can find an embedded Delaunay triangulation for a point cloud on an embedded surface in R3. CloudLSTM forecasts geospatial point-cloud data streams accurately.
problem Forecasting over geospatial point-cloud data streams.
method Introduces CloudLSTM, a recurrent neural model with a Dynamic Point-cloud Convolution (DConv) operator.
result CloudLSTM outperforms competitor models in long-term predictions for point-cloud data.
Point cloud is the most fundamental representation of 3D geometric objects. Analyzing and processing point cloud surfaces is important in computer graphics and computer vision. However, most of the existing algorithms for surface analysis require connectivity information. Therefore, it is desirable to develop a mesh st…
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" …