86 research outputs found
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime
This work examines the close interplay between cooperation and adaptation for
distributed detection schemes over fully decentralized networks. The combined
attributes of cooperation and adaptation are necessary to enable networks of
detectors to continually learn from streaming data and to continually track
drifts in the state of nature when deciding in favor of one hypothesis or
another. The results in the paper establish a fundamental scaling law for the
steady-state probabilities of miss-detection and false-alarm in the slow
adaptation regime, when the agents interact with each other according to
distributed strategies that employ small constant step-sizes. The latter are
critical to enable continuous adaptation and learning. The work establishes
three key results. First, it is shown that the output of the collaborative
process at each agent has a steady-state distribution. Second, it is shown that
this distribution is asymptotically Gaussian in the slow adaptation regime of
small step-sizes. And third, by carrying out a detailed large deviations
analysis, closed-form expressions are derived for the decaying rates of the
false-alarm and miss-detection probabilities. Interesting insights are gained.
In particular, it is verified that as the step-size decreases, the error
probabilities are driven to zero exponentially fast as functions of ,
and that the error exponents increase linearly in the number of agents. It is
also verified that the scaling laws governing errors of detection and errors of
estimation over networks behave very differently, with the former having an
exponential decay proportional to , while the latter scales linearly
with decay proportional to . It is shown that the cooperative strategy
allows each agent to reach the same detection performance, in terms of
detection error exponents, of a centralized stochastic-gradient solution.Comment: The paper will appear in IEEE Trans. Inf. Theor
Fast and Accurate ISAR Focusing Based on a Doppler Parameter Estimation Algorithm
This letter deals with inverse synthetic aperture radar (ISAR) autofocusing of noncooperative moving targets. The relative motion between the target and the sensor, which provides the angular diversity necessary for ISAR imagery, is also responsible for unwanted range migration and phase changes generating defocusing. In the case of noncooperative targets, the relative motion is unknown: the ISAR needs, hence, to implement an autofocus step [motion compensation (MoCo)] to achieve high resolution imaging. This task is typically carried out via the optimization of functionals based on general image quality parameters. In this letter, we propose the use of a fast and accurate MoCo algorithm based on the estimation of the Doppler parameters, thus fully coping with the nature of the imaging system. The effectiveness of the proposed method is proven on both simulated data and data acquired by operational systems
Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks
Data-driven methods open up unprecedented possibilities for maritime
surveillance using Automatic Identification System (AIS) data. In this work, we
explore deep learning strategies using historical AIS observations to address
the problem of predicting future vessel trajectories with a prediction horizon
of several hours. We propose novel sequence-to-sequence vessel trajectory
prediction models based on encoder-decoder recurrent neural networks (RNNs)
that are trained on historical trajectory data to predict future trajectory
samples given previous observations. The proposed architecture combines Long
Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data
and generate future predictions with different intermediate aggregation layers
to capture space-time dependencies in sequential data. Experimental results on
vessel trajectories from an AIS dataset made freely available by the Danish
Maritime Authority show the effectiveness of deep-learning methods for
trajectory prediction based on sequence-to-sequence neural networks, which
achieve better performance than baseline approaches based on linear regression
or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation
of results shows: i) the superiority of attention pooling over static pooling
for the specific application, and ii) the remarkable performance improvement
that can be obtained with labeled trajectories, i.e., when predictions are
conditioned on a low-level context representation encoded from the sequence of
past observations, as well as on additional inputs (e.g., port of departure or
arrival) about the vessel's high-level intention, which may be available from
AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and
Electronic Systems, 17 pages, 9 figure
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