SecConNet Smart

Smart, secure container networks for trusted big data sharing

In SecConNet we research novel container network architectures, which utilize programmable infrastructures and virtualisation technologies across multiple administrative domains whilst maintaining security and quality requirements of requesting parties for both private sector and scientific use-cases. For this, we exploit semantically annotated infrastructure information together with the information on the business and application logic and apply policy engines and encryption to enforce the intents of the data owners in the infrastructure and thus increasing trust.

Containers are lightweight alternatives to full-fledged virtual machines. Containers provide scientific, industrial and business applications with versatile computing environments suitable to handle Big Data applications. A container can operate as a secure, isolated and individual entity that on behalf of its owner manages and processes the data it is given.

Containers can exploit policy engines and encryption to protect algorithms and data. However, for multi-organisation (chain) applications groups of containers need access to the same data and/or need to exchange data among them. Technologies to connect containers together are developed with primary attention to their performance, but the greatest challenge is the creation of secure and reliable multi-site, multi-domain container networks.

The project will deliver multiple models of container infrastructures as archetypes for Big Data applications. SecConNet will show that containers can efficiently map to available clouds and data centers, and can be interconnected to deliver these different operational models; these in turn can support a plethora of Big Data applications in domains such as life sciences, health and industrial applications.

Participating organisations

Netherlands eScience Center
University of Amsterdam
Natural Sciences & Engineering
Natural Sciences & Engineering

Impact

Output

Team

PG
Paola Grosso
Principal investigator
University of Amsterdam
Rena Bakhshi
Programme Manager
Netherlands eScience Center
SS
Sara Shakeri
PhD student
University of Amsterdam

Related projects

CARRIER

Coronary artery disease: risk estimations and interventions for prevention and Early detection

Updated 3 months ago
In progress

MyDigiTwin

Using big data to put a cardiovascular digital twin into the hands of people

Updated 8 months ago
In progress

CHEOPS

Verified construction of correct and optimised parallel software

Updated 25 months ago
Finished

Enhance Your Research Alliance (EYRA) Benchmark Platform

Supporting researchers to easily set-up benchmarks

Updated 21 months ago
Finished

PROCESS

Providing computing solutions for exascale challenges

Updated 25 months ago
Finished

City Cloud

From the Things to the Cloud and back

Updated 21 months ago
Finished

High spatial resolution phenological modelling at continental scales

Understanding phenological variability

Updated 1 month ago
Finished

IMPACT

Software analytics for the monitoring and assessment of the global impact of eScience software on...

Updated 21 months ago
Finished

A methodology and ecosystem for many-core programming

Boosting the performance of current and future programs

Updated 21 months ago
Finished

Visual Storytelling of Big Imaging Data

Storytelling as a means of visual data communication

Updated 23 months ago
Finished

Enabling Dynamic Services

Realizing the full potential of the Dutch e-Infrastructure

Updated 21 months ago
Finished

RT SAR

An architecture for real Time big data processing for AMBER

Updated 21 months ago
Finished

A Jungle Computing Approach to Large-Scale Online Forensic Analysis

Programming tools that simplify application development and deployment

Updated 21 months ago
Finished

e-Visualization of Big Data

Interactive big data visualizations

Updated 22 months ago
Finished

Massive Point Clouds for eSciences

Using point clouds to their full potential

Updated 21 months ago
Finished

Generic eScience Technologies

Making breakthroughs in data-driven research

Updated 21 months ago
Finished

eSiBayes

An eScience infrastructure for Bayesian inverse modeling

Updated 21 months ago
Finished

Related software

Mahiru

MA

A prototype for a federated, distributed data sharing and processing system.

Updated 6 months ago
1 2