A Trusted Data Storage Infrastructure for Grid-Based Medical Applications

2008 ◽  
Author(s):  
Guido J. van 't Noordende ◽  
Silvia D. Olabarriaga ◽  
Matthijs R. Koot ◽  
Cees Th.A.M. de Laat
Keyword(s):  
Data Storage ◽  
Medical Applications ◽  
Trusted Data ◽  
Grid Based

A Trusted Data Storage Infrastructure for Grid-Based Medical Applications

2009 ◽  
Vol 1 (2) ◽  
pp. 1-14
Author(s):  
Guido J. van ‘t Noordende ◽  
Silvia D. Olabarriaga ◽  
Matthijs R. Koot ◽  
Cees Th. A.M. de Laat
Keyword(s):  
Data Storage ◽  
Medical Applications ◽  
Trusted Data ◽  
Grid Based

Trusted Data Management for Grid-Based Medical Applications

2011 ◽  
pp. 208-220
Author(s):  
Guido J. van ’t Noordende ◽  
Silvia D. Olabarriaga ◽  
Matthijs R. Koot ◽  
Cees Th. A.M. de Laat
Keyword(s):  
Data Management ◽  
Medical Applications ◽  
Grid Technology ◽  
Sensitive Information ◽  
Security Framework ◽  
Privacy And Security ◽  
Grid Middleware ◽  
Design And Implementation ◽  
Trusted Data ◽  
Grid Based

Existing Grid technology has been foremost designed with performance and scalability in mind. When using Grid infrastructure for medical applications, privacy and security considerations become paramount. Privacy aspects require a re-thinking of the design and implementation of common Grid middleware components. This chapter describes a novel security framework for handling privacy sensitive information on the Grid, and describes the privacy and security considerations which impacted its design.

A Trusted Data Storage and Access Control Scheme for Power CPS Combining Blockchain and Attribute-Based Encryption

2021 ◽  
Author(s):  
Zhang Geng ◽  
Wang Yanan ◽  
Liu Guojing ◽  
Wang Xueqing ◽  
Gao Kaiqiang ◽  
...  
Keyword(s):  
Access Control ◽  
Data Storage ◽  
Attribute Based Encryption ◽  
Control Scheme ◽  
Trusted Data

scholarly journals Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2410
Author(s):  
Muhammad Firdaus ◽  
Sandi Rahmadika ◽  
Kyung-Hyune Rhee
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Traffic Safety ◽  
Network Performance ◽  
Intelligent Transportation System ◽  
Edge Computing ◽  
The Internet ◽  
Smart Vehicles ◽  
The Incentive Mechanism ◽  
Trusted Data

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

Integrity protection method for trusted data of IOT nodes based on transfer learning

2021 ◽  
pp. 1-11
Author(s):  
Lin Tang
Keyword(s):  
Transfer Learning ◽  
Data Storage ◽  
Learning Algorithm ◽  
Homomorphic Encryption ◽  
Mapping Function ◽  
Data Encryption ◽  
Complete Data ◽  
Protection Method ◽  
Integrity Protection ◽  
Trusted Data

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IOT node, this paper proposes an integrity protection method for trusted data of IOT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IOT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IOT nodes. The data of the IOT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IOT nodes, and embedded processor is added to the IOT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IOT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IOT nodes is about 3 s, and the work efficiency is high.

scholarly journals Combination of sensor-embedded and secure server-distributed artificial intelligence for healthcare applications

2019 ◽  
Vol 5 (1) ◽  
pp. 29-32 ◽  
Author(s):  
Pierre Gembaczka ◽  
Burkhard Heidemann ◽  
Bernhard Bennertz ◽  
Wolfgang Groeting ◽  
Thomas Norgall ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Data Storage ◽  
Distributed Artificial Intelligence ◽  
Sensor Data ◽  
Sensor Calibration ◽  
Healthcare Applications ◽  
Related Data ◽  
Calibration Pattern ◽  
Trusted Data

AbstractThe application of artificial intelligence (AI) in the areas of health, care and social participation offers great opportunities but also involves great challenges. Extensive regulatory, ethical and data-security related requirements exist for data recording, storage and processing of respective personalized and patient-related data. “Artificial Intelligence as a Service” (AIaaS) is pushed for consumer applications by global players, which implies data storage on external database server. However, the available solutions do not meet the requirements. Moreover, small and medium-sized enterprises (SMEs) in the field of healthcare fear the loss of data sovereignty and information outflow. In this paper, we propose a secure and resource-efficient approach by embedding AI directly close to the sensor in combination with secure and distributed data processing on local server or certified “Trusted Data Center”. For this purpose, we have developed the Artificial Intelligence for Embedded Systems (AIfES) platform-independent machine learning library in C programming language. It contains a fully configurable deep artificial neural network with feedforward structure. The library can be run directly on a microcontroller and even allows to train the neural network. Possible healthcare applications include direct (pre-) processing of sensor data, sensor calibration, pattern recognition and classification.

The First Grid for the Oral and Maxillofacial Region and Its Application for Speech Analysis

2005 ◽  
Vol 44 (02) ◽  
pp. 253-256 ◽  
Author(s):  
T. Akiyama ◽  
H. Tamagawa ◽  
S. Kato ◽  
Y. Mizuno-Matsumoto ◽  
M. Nakagawa ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Data Storage ◽  
Function Analysis ◽  
Three Dimensional ◽  
Speech Disorders ◽  
Data Management System ◽  
Semantic Data ◽  
Computational Fluid Dynamics Analysis ◽  
Grid Based

Summary Objectives: Introduction of a new grid-based method for analyzing speech functions which takes into account the related information of patients’ data and the oral air flow with pronouncing analyzed by computational fluid dynamics. Methods: An on-line speech analyzer was developed for clinical use utilizing GridPort2.3.1 based on glo-bus2.4.2, comprising several computational tools such as unified data storage, semantic data analysis, computational fluid dynamics analysis and three-dimensional visualization of calculated results from different hardware sources with various types of operation systems. Results: The power transportation layer between dental clinics and computational and storage resources could be provided by using a WWW-based portal. The back-end data management system could be constructed using a storage resource broker (SRB) and extensible mark up language (XML). Conclusions: The new method allows the construction of a data warehouse through this grid-based speech function analysis in order to extract the principal factors related to speech disorders.

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