scholarly journals Representing Model Discrepancy in Bound-to-Bound Data Collaboration

2021 ◽  
Vol 9 (1) ◽  
pp. 231-259
Author(s):  
Wenyu Li ◽  
Arun Hegde ◽  
James Oreluk ◽  
Andrew Packard ◽  
Michael Frenklach
Keyword(s):  
Model Discrepancy ◽  
Data Collaboration

scholarly journals Adaptive Secure Cross-Cloud Data Collaboration with Identity-Based Cryptography and Conditional Proxy Re-Encryption

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qinlong Huang ◽  
Yue He ◽  
Wei Yue ◽  
Yixian Yang
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Security And Privacy ◽  
Broadcast Encryption ◽  
Cloud Data ◽  
Identity Based ◽  
Identity Based Cryptography ◽  
And Performance ◽  
Cryptographic Techniques ◽  
Data Collaboration

Data collaboration in cloud computing is more and more popular nowadays, and proxy deployment schemes are employed to realize cross-cloud data collaboration. However, data security and privacy are the most serious issues that would raise great concerns from users when they adopt cloud systems to handle data collaboration. Different cryptographic techniques are deployed in different cloud service providers, which makes cross-cloud data collaboration to be a deeper challenge. In this paper, we propose an adaptive secure cross-cloud data collaboration scheme with identity-based cryptography (IBC) and proxy re-encryption (PRE) techniques. We first present a secure cross-cloud data collaboration framework, which protects data confidentiality with IBC technique and transfers the collaborated data in an encrypted form by deploying a proxy close to the clouds. We then provide an adaptive conditional PRE protocol with the designed full identity-based broadcast conditional PRE algorithm, which can achieve flexible and conditional data re-encryption among ciphertexts encrypted in identity-based encryption manner and ciphertexts encrypted in identity-based broadcast encryption manner. The extensive analysis and experimental evaluations demonstrate the well security and performance of our scheme, which meets the secure data collaboration requirements in cross-cloud scenarios.

scholarly journals Laying the Foundation for Effective Partnerships: An Examination of Data Sharing Agreements

2020 ◽  
Author(s):  
Hayden Dahmm
Keyword(s):  
Data Sharing ◽  
Sensitive Data ◽  
Future Data ◽  
World Economic ◽  
Key Issues ◽  
Online Library ◽  
The University ◽  
Sector Data ◽  
Selection Of ◽  
Data Collaboration

In the midst of the COVID-19 pandemic, data has never been more salient. COVID has generated new data demands and increased cross-sector data collaboration. Yet, these data collaborations require careful planning and evaluation of risks and opportunities, especially when sharing sensitive data. Data sharing agreements (DSAs) are written agreements that establish the terms for how data are shared between parties and are important for establishing accountability and trust. However, negotiating DSAs is often time consuming, and collaborators lacking legal or financial capacity are disadvantaged. Contracts for Data Collaboration (C4DC) is a joint initiative between SDSN TReNDS, NYU’s GovLab, the World Economic Forum, and the University of Washington, working to strengthen trust and transparency of data collaboratives. The partners have created an online library of DSAs which represents a selection of data applications and contexts. This report introduces C4DC and its DSA library. We demonstrate how the library can support the data community to strengthen future data collaborations by showcasing various DSA applications and key considerations. First, we explain our method of analyzing the agreements and consider how six major issues are addressed by different agreements in the library. Key issues discussed include data use, access, breaches, proprietary issues, publicization of the analysis, and deletion of data upon termination of the agreement. For each of these issues, we describe approaches illustrated with examples from the library. While our analysis suggests some pertinent issues are regularly not addressed in DSAs, we have identified common areas of practice that may be helpful for entities negotiating partnership agreements to consider in the future.

scholarly journals ‘Big Data’ collaboration: Exploring, recording and sharing enterprise knowledge

2013 ◽  
Vol 33 (3-4) ◽  
pp. 257-270 ◽  
Author(s):  
Sreenivas R. Sukumar ◽  
Regina K. Ferrell
Keyword(s):  
Big Data ◽  
Data Collaboration

Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques

2020 ◽  
Author(s):  
H. Nandi Formentin ◽  
I. Vernon ◽  
M. Goldstein ◽  
C. Caiado ◽  
G. Avansi ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Uncertainty Analysis ◽  
Model Discrepancy

scholarly journals Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration

2018 ◽  
Vol 6 (2) ◽  
pp. 429-456 ◽  
Author(s):  
Arun Hegde ◽  
Wenyu Li ◽  
James Oreluk ◽  
Andrew Packard ◽  
Michael Frenklach
Keyword(s):  
Consistency Analysis ◽  
Data Collaboration

Mixtures of experts for understanding model discrepancy in dynamic computer models

2014 ◽  
Vol 71 ◽  
pp. 491-505 ◽  
Author(s):  
David J. Nott ◽  
Lucy Marshall ◽  
Mark Fielding ◽  
Shie-Yui Liong
Keyword(s):  
Computer Models ◽  
Mixtures Of Experts ◽  
Model Discrepancy
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