Towards a Modular On-Premise Approach for Data Sharing

Abstract

The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users’ control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.

Expanding the boundaries of previously obtained informed consent in research: Views from participants in the Personalised Risk‐based Mammascreening study

“…Cloud computing and virtualisation, however, are rapidly becoming the norm in the health sector. 26 , 27 , 28 These advances have given rise to digital research environments, which permit secure, auditable data sharing and analysis within a virtual workspace, 29 eliminating the need to transfer data between local hard drives. In the Netherlands, the AnDREa project is one of the numerous working examples of such endeavours, 29 and PRISMA data will be accessible within this environment.…”

“…Mechanisms for data sharing and maintenance vary, with some more vulnerable to security risks than others. Cloud computing and virtualisation, however, are rapidly becoming the norm in the health sector 26–28 . These advances have given rise to digital research environments, which permit secure, auditable data sharing and analysis within a virtual workspace, 29 eliminating the need to transfer data between local hard drives.…”

Research on Monitoring Data Security Sharing Method for Hydropower Station Operation and Maintenance

“…In order to improve the intelligent level of hydropower station, it is urgent to effectively integrate the data from various sources and build an integrated monitoring data platform to provide data support for the whole intelligent hydropower station. Various business function modules of the information platform can be combined freely according to the actual needs to avoid the waste of resources; Different intelligent electronic devices (IED) can carry out fast and accurate information communication, use artificial intelligence technology for intelligent analysis and comprehensive processing of power plant events, and provide man-machine interface to meet the needs of user interaction [2][3]. In order to meet the diversified needs of various engineering applications of intelligent hydropower station for state data, six levels of state data are formed through the analysis and statistical calculation of different time scales, as shown in Figure 1.…”

“…In formula (4), 2 F represents the routing score; 1 n indicates the number of IP addresses in the route; 2 n represents the weighted sum of IP scores all IP addresses. Through the routing score, the probability density distribution is calculated by using kernel density estimation.…”