2,304 research outputs found
DeepSecure: Scalable Provably-Secure Deep Learning
This paper proposes DeepSecure, a novel framework that enables scalable
execution of the state-of-the-art Deep Learning (DL) models in a
privacy-preserving setting. DeepSecure targets scenarios in which neither of
the involved parties including the cloud servers that hold the DL model
parameters or the delegating clients who own the data is willing to reveal
their information. Our framework is the first to empower accurate and scalable
DL analysis of data generated by distributed clients without sacrificing the
security to maintain efficiency. The secure DL computation in DeepSecure is
performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized
realization of various components used in DL. Our optimized implementation
achieves more than 58-fold higher throughput per sample compared with the
best-known prior solution. In addition to our optimized GC realization, we
introduce a set of novel low-overhead pre-processing techniques which further
reduce the GC overall runtime in the context of deep learning. Extensive
evaluations of various DL applications demonstrate up to two
orders-of-magnitude additional runtime improvement achieved as a result of our
pre-processing methodology. This paper also provides mechanisms to securely
delegate GC computations to a third party in constrained embedded settings
Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits. On
MNIST and Fashion datasets, our learning algorithm outperforms neural networks
that have an order of magnitude more parameters. Yet, logistic circuits have a
distinct origin in symbolic AI, forming a discriminative counterpart to
probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that
parameter learning for logistic circuits is convex optimization, and that a
simple local search algorithm can induce strong model structures from data.Comment: Published in the Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI19
A Compression Technique Exploiting References for Data Synchronization Services
Department of Computer Science and EngineeringIn a variety of network applications, there exists significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.4%, 20.1%, 29.9%, and 61.2%, and are about 78.3%, 79.6%, 86.1%, and 92.9% in a web browsing scenario, respectively.ope
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Are we using the right potential functions in the Conditional Random Field
models that are popular in the Vision community? Semantic segmentation and
other pixel-level labelling tasks have made significant progress recently due
to the deep learning paradigm. However, most state-of-the-art structured
prediction methods also include a random field model with a hand-crafted
Gaussian potential to model spatial priors, label consistencies and
feature-based image conditioning.
In this paper, we challenge this view by developing a new inference and
learning framework which can learn pairwise CRF potentials restricted only by
their dependence on the image pixel values and the size of the support. Both
standard spatial and high-dimensional bilateral kernels are considered. Our
framework is based on the observation that CRF inference can be achieved via
projected gradient descent and consequently, can easily be integrated in deep
neural networks to allow for end-to-end training. It is empirically
demonstrated that such learned potentials can improve segmentation accuracy and
that certain label class interactions are indeed better modelled by a
non-Gaussian potential. In addition, we compare our inference method to the
commonly used mean-field algorithm. Our framework is evaluated on several
public benchmarks for semantic segmentation with improved performance compared
to previous state-of-the-art CNN+CRF models.Comment: Presented at EMMCVPR 2017 conferenc
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
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