scholarly journals Integrating hydrologic modeling web services with online data sharing to prepare, store, and execute hydrologic models

2020 ◽  
Vol 130 ◽  
pp. 104731 ◽  
Author(s):  
Tian Gan ◽  
David G. Tarboton ◽  
Pabitra Dash ◽  
Tseganeh Z. Gichamo ◽  
Jeffery S. Horsburgh
Keyword(s):  
Web Services ◽  
Data Sharing ◽  
Hydrologic Modeling ◽  
Hydrologic Models ◽  
Online Data

Efficient Attribute-Based Data Sharing Scheme with Hidden Access Structures

2019 ◽  
Vol 62 (12) ◽  
pp. 1748-1760 ◽  
Author(s):  
Yang Chen ◽  
Wenmin Li ◽  
Fei Gao ◽  
Wei Yin ◽  
Kaitai Liang ◽  
...  
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Inner Product ◽  
Access Structure ◽  
Online Data ◽  
Fine Grained ◽  
Access Structures ◽  
Attribute Based Encryption ◽  
And Performance ◽  
Ciphertext Policy

AbstractOnline data sharing has become a research hotspot while cloud computing is getting more and more popular. As a promising encryption technique to guarantee the security shared data and to realize flexible fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has drawn wide attentions. However, there is a drawback preventing CP-ABE from being applied to cloud applications. In CP-ABE, the access structure is included in the ciphertext, and it may disclose user’s privacy. In this paper, we find a more efficient method to connect ABE with inner product encryption and adopt several techniques to ensure the expressiveness of access structure, the efficiency and security of our scheme. We are the first to present a secure, efficient fine-grained access control scheme with hidden access structure, the access structure can be expressed as AND-gates on multi-valued attributes with wildcard. We conceal the entire attribute instead of only its values in the access structure. Besides, our scheme has obvious advantages in efficiency compared with related schemes. Our scheme can make data sharing secure and efficient, which can be verified from the analysis of security and performance.

An SOA-Based Architecture to Share Medical Data with Privacy Preservation

2011 ◽  
Vol 2 (3) ◽  
pp. 11-26
Author(s):  
Mahmoud Barhamgi ◽  
Djamal Benslimane ◽  
Chirine Ghedira ◽  
Brahim Medjahed
Keyword(s):  
Web Services ◽  
Web Service ◽  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Medical Data ◽  
Query Rewriting ◽  
Healthcare Organizations ◽  
Privacy Constraints ◽  
Viable Approach

Recent years have witnessed a growing interest in using Web services as a reliable means for medical data sharing inside and across healthcare organizations. In such service-based data sharing environments, Web service composition emerged as a viable approach to query data scattered across independent locations. Patient data privacy preservation is an important aspect that must be considered when composing medical Web services. In this paper, the authors show how data privacy can be preserved when composing and executing Web services. Privacy constraints are expressed in the form of RDF queries over a mediated ontology. Query rewriting algorithms are defined to process those queries while preserving users’ privacy.

scholarly journals Differential Expression Gene Explorer (DrEdGE): A tool for generating interactive online data visualizations for exploration of quantitative transcript abundance datasets

2019 ◽  
Author(s):  
Sophia C. Tintori ◽  
Patrick Golden ◽  
Bob Goldstein
Keyword(s):  
Data Sharing ◽  
Transcript Abundance ◽  
Rna Seq ◽  
Online Data ◽  
Web Based ◽  
Neuronal Tissue ◽  
C Elegans ◽  
Differential Expression Gene ◽  
Public Data ◽  
Data Visualizations

AbstractAs the scientific community becomes increasingly interested in data sharing, there is a growing need for tools that facilitate the querying of public data. Mining of RNA-seq datasets, for example, has value to many biomedical researchers, yet is often effectively inaccessible to non-genomicist experts, even when the raw data are available. Here we present DrEdGE (dredge.bio.unc.edu), a free Web-based tool that facilitates data sharing between genomicists and their colleagues. The DrEdGE software guides genomicists through easily creating interactive online data visualizations, which colleagues can then explore and query according to their own conditions to discover genes, samples, or patterns of interest. We demonstrate DrEdGE’s features with three example websites we generated from publicly available datasets—human neuronal tissue, mouse embryonic tissue, and a C. elegans embryonic series. DrEdGE increases the utility of large genomics datasets by removing the technical obstacles that prevent interested parties from exploring the data independently.

scholarly journals Examining cross-scale influences of forcing resolutions in a hillslope-resolving, integrated hydrologic model

2020 ◽  
Author(s):  
Miguel A. Aguayo ◽  
Alejandro N. Flores ◽  
James P. McNamara ◽  
Hans-Peter Marshall ◽  
Jodi Mead
Keyword(s):  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Water Storage ◽  
Hydrologic Model ◽  
Atmospheric Forcing ◽  
Hydrologic Models ◽  
Snow Water ◽  
Integrated Hydrologic Model ◽  
The Impact

Abstract. Water management in semiarid regions of the western United States requires accurate and timely knowledge of runoff generated by snowmelt. This information is used to plan reservoir releases for downstream users and hydrologic models play an important role in estimating the volume of snow stored in mountain watersheds that serve as source waters for downstream reservoirs. Physically based, integrated hydrologic models are used to develop spatiotemporally dynamic estimates of hydrologic states and fluxes based on understanding of the underlying biophysics of hydrologic response. Yet this class of models are associated with many issues that give rise to significant uncertainties in key hydrologic variables of interest like snow water storage and streamflow. Underlying sources of uncertainty include difficulties in parameterizing processes associated with nonlinearities of some processes, as well as from the large variability in the characteristic spatial and temporal scale of atmospheric forcing and land-surface water and energy balance and groundwater processes. Scale issues, in particular, can introduce systematic biases in integrated atmospheric and hydrologic modeling. Reconciling these discrepancies while maintaining computational tractability remains a fundamental challenge in integrated hydrologic modeling. Here we investigate the hydrologic impact of discrepancies between distributed meteorological forcing data exhibiting a range of spatial scales consistent with a variety of numerical weather prediction models when used to force an integrated hydrologic model associated with a corresponding range of spatial resolutions characteristic of distributed hydrologic modeling. To achieve this, we design and conduct a total of twelve numerical modeling experiments that seek to quantify the impact of applied resolution of atmospheric forcings on simulated hillslope-scale hydrologic state variables. The experiments are arranged in such way to assess the impact of four different atmospheric forcing resolutions (i.e., interpolated 30 m, 1 km, 3 km and 9 km) on two hydrologic variables, snow water equivalent and soil water storage, arranged in three hydrologic spatial resolution (i.e., 30 m, 90 m and 250 m). Results show spatial patterns in snow water equivalent driven by atmospheric forcing in hillslope-scale simulations and patterns mostly driven by topographical characteristics (i.e., slope and aspect) on coarser simulations. Similar patterns are observed in soil water storage however, in addition to that, large errors are encountered primarily in riparian areas of the watershed on coarser simulations. The Weather Research Forecasting (WRF) model is used to develop the environmental forcing variables required as input to the integrated hydrologic model. WRF is an open source, community supported coupled land-atmosphere model capable of capturing spatial scales that permit convection. The integrated hydrologic modeling framework used in this work coincides with the ParFlow open-source surface-subsurface hydrology model. This work has important implications for the use of atmospheric and integrated hydrologic models in remote and ungauged areas. In particular, this work has potential ramifications for the design and development of observing system simulation experiments (OSSEs) in complex and snow-dominated landscapes. OSSEs are critical in constraining the performance characteristics of Earth-observing satellites.

scholarly journals Secure Online Data Sharing in Cloud by Private Transmit Cryptosystem Using Aggregate Keys

2018 ◽  
Vol 7 (3.27) ◽  
pp. 36
Author(s):  
E Amarnath Reddy ◽  
M Srinuvasa Reddy ◽  
Kompally Manisha ◽  
B Mamatha
Keyword(s):  
Data Sharing ◽  
Hash Functions ◽  
Data Integrity ◽  
Data Retrieval ◽  
Data Loss ◽  
Online Data ◽  
The Stability ◽  
Crucial Part

Cloud has become a crucial part of our day to day life because of its easy, effortless and straightforward nature of data storing and sharing. One of the important concerns for many users is data storing, we mustn’t forget about data sharing. A convenient way of online data sharing is to look at its pros for simple access while preserving security are cons for any user. Thus, a better way of ensuring user’s data is to implement data integrity with the KAC scheme. This scheme provides an efficient sharing method of decrypting multiple sets of data with the single key. It’s one among many ways of quick and effective data retrieval in case of data loss or data alteration on the cloud. It also uses the broadcast algorithm to distribute data for a specific set of users. This scheme uses basic HMAC, one of the secure hash functions for the stability of data integrity.  Therefore provides a protected environment where a user can share the bulk of data through integrity. Another advantage of using this scheme could reduce the burden of computation over the cloud.       

HydroNets: Leveraging River Network Structure and Deep Neural Networks for Hydrologic Modeling

2020 ◽  
Author(s):  
Zach Moshe ◽  
Asher Metzger ◽  
Frederik Kratzert ◽  
Efrat Morin ◽  
Sella Nevo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Expert Systems ◽  
Prior Knowledge ◽  
Hydrologic Modeling ◽  
Network Connectivity ◽  
General Purpose ◽  
River Network ◽  
Hydrologic Models ◽  
Site Specific

<p>Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. In this work we present a novel family of hydrologic models, called HydroNets, that leverages river network connectivity structure within deep neural architectures. The injection of this connectivity structure prior knowledge allows for scalable and accurate hydrologic modeling.</p><p>Prior knowledge plays an important role in machine learning and AI. On one extreme of the prior knowledge spectrum there are expert systems, which exclusively rely on domain expertise encoded into a model. On the other extreme there are general purpose agnostic machine learning methods, which are exclusively data-driven, without intentional utilization of inductive bias for the problem at hand. In the context of hydrologic modeling, conceptual models such as the Sacramento Soil Moisture Accounting Model (SAC-SMA) are closer to expert systems. Such models require explicit functional modeling of water volume flow in terms of their input variables and model parameters (e.g., precipitation, hydraulic conductivity, etc.) which could be calibrated using data. Instances of agnostic methods for stream flow hydrologic modelling, which for the most part do not utilize problem specific bias, have recently been presented by Kratzert et al. (2018, 2019) and by Shalev et al. (2019). These works showed that general purpose deep recurrent neural networks, such as long short-term models (LSTMs), can achieve state-of-the-art hydrologic forecasts at scale with less information.</p><p>One of the fundamental reasons for the success of deep neural architectures in most application domains is the incorporation of prior knowledge into the architecture itself. This is, for example, the case in machine vision where convolutional layers and max pooling manifest essential invariances of visual perception. In this work we present HydroNets, a family of neural network models for hydrologic forecasting. HydroNets leverage the inherent (graph-theoretic) tree structure of river water flow, existing in any multi-site hydrologic basin. The network architecture itself reflects river network connectivity and catchment structures such that each sub-basin is represented as a tree node, and edges represent water flow from sub-basins to their containing basin. HydroNets are constructed such that all nodes utilize a shared global model component, as well as site-specific sub-models for local modulations. HydroNets thus combine two signals: site specific rainfall-runoff and upstream network dynamics, which can lead to improved predictions at longer horizons. Moreover, the proposed architecture, with its shared global model, tend to reduce sample complexity, increase scalability, and allows for transferability to sub-basins that suffer from scarce historical data. We present several simulation results over multiple basins in both India and the USA that convincingly support the proposed model and its advantages.</p>

scholarly journals Four-way handshake protocol for authenticating in multiple mix-zones

2018 ◽  
Vol 7 (2.4) ◽  
pp. 200
Author(s):  
T Senthil Kumar ◽  
S Prabhakaran ◽  
V Prashanth
Keyword(s):  
Mobile Devices ◽  
Data Sharing ◽  
Mobile Networks ◽  
Personal Information ◽  
Side Information ◽  
Authentication Protocol ◽  
Zone Model ◽  
Authentication Protocols ◽  
Online Data ◽  
Handshake Protocol

Authentication is the process of verifying that the users who they claim to be or not, it is based on identity and credentials. Most of the attacks can be reduced using authentication process. Authentication is important because as the amount of online data sharing has increased, threats and fraud in a large amount are also increased, a changing of the guard which provides security to mobile devices is needed for which authentication is necessary. Privacy of user’s location is important in mobile networks, there are several strategies to protect the personal information (i.e., their location). In previous work it is introduced that the mix zone model which will change the old pseudonyms to new pseudonyms and anonymizes user’s identity by restricting the position where users can be located. Later work, even in the multiple mix-zones model, attackers can attack by using side information (like footprints, navigation etc.). So, we need an authentication protocol while two mix-zones or user-services are communicating. We came across different authentication protocols like PAP, CHAP, and EAP. In this paper, a four-way handshake protocol is implemented for providing authentication while multiple mix-zones are communicating. A four-way handshake authentication protocol i.e., WPA-PSK protocol for verification. WPA-PSK is applied in such a way that both STA(supplicant) and AP(authenticator) can check that they are re-agreeing on a non-forged RSN and IE, therefore they are using the most secure available protocols. 

scholarly journals A Method for Evaluating the Accuracy of Quantitative Precipitation Estimates from a Hydrologic Modeling Perspective

2005 ◽  
Vol 6 (2) ◽  
pp. 115-133 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Baxter E. Vieux
Keyword(s):  
Hydrologic Modeling ◽  
Initial Conditions ◽  
Rain Gauge ◽  
Streamflow Prediction ◽  
Hydrologic Models ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Primary Focus ◽  
Quantitative Precipitation Estimates

Abstract A major goal in quantitative precipitation estimation and forecasting is the ability to provide accurate initial conditions for the purposes of hydrologic modeling. The accuracy of a streamflow prediction system is dependent upon how well the initial hydrometeorological states are characterized. A methodology is developed to objectively and quantitatively evaluate the skill of several different precipitation algorithms at the scale of application—a watershed. Thousands of hydrologic simulations are performed in an ensemble fashion, enabling an exploration of the model parameter space. Probabilistic statistics are then utilized to compare the relative skill of hydrologic simulations produced from the different precipitation inputs to the observed streamflow. The primary focus of this study is to demonstrate a methodology to evaluate precipitation algorithms that can be used to supplement traditional radar–rain gauge analyses. This approach is appropriate for the evaluation of precipitation estimates or forecasts that are intended to serve as inputs to hydrologic models.

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