13,533 research outputs found
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes
Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de EconomĂa, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-
Improving Information Technology(IT) Risk Analysis with Resource Allocation
Improving Information Technology(IT) Risk Analysis with Resource Allocatio
RETURNS TO SORTING AND MARKET TIMING OF ANIMALS WITHIN PENS OF FED CATTLE
This research examines returns to cattle feeding operations that sort animals prior to marketing using ultrasound technology. The returns to sorting are between 25 per head depending on the number of groups the pens into which cattle can be sorted. Sorting faces declining returns. These returns can also be viewed as the costs imposed by institutional constraints that limit co-mingling of cattle. Through sorting, cattle feeding operations are able to reduce meat quality discounts, increase meat quality premiums, increase beef carcass quality characteristics, more efficiently use feed resources, and increase profits.Livestock Production/Industries,
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Today, the most widespread, widely applicable technology for gathering data
relies on experienced scientists armed with handheld radio telemetry equipment
to locate low-power radio transmitters attached to wildlife from the ground.
Although aerial robots can transform labor-intensive conservation tasks, the
realization of autonomous systems for tackling task complexities under
real-world conditions remains a challenge. We developed ConservationBots-small
aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial
robot achieves robust localization performance and fast task completion times
-- significant for energy-limited aerial systems while avoiding close
encounters with potential, counter-productive disturbances to wildlife. Our
approach overcomes the technical and practical problems posed by combining a
lightweight sensor with new concepts: i) planning to determine both trajectory
and measurement actions guided by an information-theoretic objective, which
allows the robot to strategically select near-instantaneous range-only
measurements to achieve faster localization, and time-consuming sensor rotation
actions to acquire bearing measurements and achieve robust tracking
performance; ii) a bearing detector more robust to noise and iii) a tracking
algorithm formulation robust to missed and false detections experienced in
real-world conditions. We conducted extensive studies: simulations built upon
complex signal propagation over high-resolution elevation data on diverse
geographical terrains; field testing; studies with wombats (Lasiorhinus
latifrons; nocturnal, vulnerable species dwelling in underground warrens) and
tracking comparisons with a highly experienced biologist to validate the
effectiveness of our aerial robot and demonstrate the significant advantages
over the manual method.Comment: 33 pages, 21 figure
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