Efficient Privacy-Preserving Spatial Data Query in Cloud Computing

Miao, Yinbin; Yang, Yutao; Li, Xinghua; Wei, Linfeng; Liu, Zhiquan; Deng, Robert H.

doi:10.1109/tkde.2023.3283020

Cited by 16 publications

(3 citation statements)

References 41 publications

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“…Ultimately, our present study has yet to delve extensively into the intricacies of security and privacy considerations within the interactions involving a system using the proposed model and users. Exploring perspectives from recent publications, such as the works by Miao et al employing Enhanced Asymmetric Scalar-Product-Preserving Encryption for querying data [87] and a time-controllable keyword search scheme [88], gives us promising directions to transform our current proposal to a more comprehensive, industry-ready solution.…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds

Simić,

Dedeić,

Stojkov

et al. 2024

IEEE Access

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The micro cloud model offers cloud behavior at the edge of the network. It allows dynamic organization of the resources, closer to the users and the data. One of the crucial problems to solve is to design a proper cloud model at the edge of the network and offer cloud services that support multi-tenancy. This cloud property is a governing mechanism to lower cloud costs. It is essential for the scalability of both public and private clouds due to the utilization of shared resources and the logical separation of tenants. This paper presents the model for the creation of virtual clouds (vClouds) on physical infrastructure using a hierarchy of namespaces, with proper organization and redistribution of resources such as CPU, RAM, and storage, while preserving logical isolation between vClouds, thus creating a multi-tenant system. The presented model guarantees accurate resource redistribution through graph transformations to model operations, while the proposed protocols ensure correctness by employing multiparty session types for modeling. We extend the secure computing mode to establish an isolated system, allowing sandboxing rules for every namespace and creating hierarchies of security profiles. This advancement enables our model to inherit parent security profiles fully, extend them by adding child-specific elements, or redefine and create entirely new ones. Furthermore, the users can switch context, meaning they can change vCloud or the namespace they operate on.INDEX TERMS Cloud computing, distributed systems, edge computing, formal specifications, infrastructure as software, platform, namespaces, isolation, multi-tenancy.• We enable users to switch between contexts, meaning they can change vCloud or the namespace they operate on.These advancements rely on our following technical contributions:• Protocol definitions for namespace manipulation:This study defines protocols for performing crucial operations, such as creating, updating, and deleting namespaces within virtual clouds, leveraging the power of rules for graph transformations over namespaces. These protocols provide a standardized framework for managing and manipulating namespaces effectively.

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Section: Discussionmentioning

confidence: 99%

A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds

Simić,

Dedeić,

Stojkov

et al. 2024

IEEE Access

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“…It is important to have robust data management in place to facilitate the decentralization of the operations and also prevent system failures, which will be achieved by this combination (cloud computing and blockchain) and by distributing data processing across multiple cloud servers. Many works in different disciplines highlighted the importance of the role of crowdsourced data in the cloud for more capabilities and scalability [26][27][28].…”

Section: Cloud-based Gis Soarsmentioning

confidence: 99%

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

Alamri

2024

IJGI

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Crowdsourced spatial analytics is a rapidly developing field that involves collecting and analyzing geographical data, utilizing the collective power of human observation. This paper explores the field of spatial data analytics and crowdsourcing and how recently developed tools, cloud-based GIS, and artificial intelligence (AI) are being applied in this domain. This paper examines and discusses cutting-edge technologies and case studies in different fields of spatial data analytics and crowdsourcing used in a wide range of industries and government departments such as urban planning, health, transportation, and environmental sustainability. Furthermore, by understanding the concerns associated with data quality and data privacy, this paper explores the potential of crowdsourced data while also examining the related problems. This study analyzes the obstacles and challenges related to “geospatial crowdsourcing”, identifying significant limitations and predicting future trends intended to overcome the related challenges.

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“…The resources are considered a major source of power consumption, like air conditioning and cooling down various equipment [25]. Moreover, according to research, power consumption is proportional to resource utilization, and data centers are considered the world's highest consumers of electricity [26], [27]. So, power monitoring tools play a vital role in cloud data centers in a way that energy consumption can be minimized as virtual cloud computing platform virtualization has a very wide range of access.…”

Section: Introductionmentioning

confidence: 99%

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Algarni,

Shah,

Jehangiri

et al. 2024

IEEE Access

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Cloud computing infrastructure is designed to deploy and assess service-oriented applications, primarily via cloud datacenters. These datacenters are integral to energy utilization in cloud environments, with energy consumption closely tied to resource utilization. It is important to monitor and predict power consumption in these datacenters, especially for high-demand services. Container-based virtualization, particularly using Docker containers, has gained significant attention due to its lightweight nature. However, predicting energy usage at a fine-grained level for container-based applications is a challenging task. In this study, we employ three time series analysis algorithms-AR, ARIMA, and ETS-to predict the energy usage of Docker containers over the next hour. Utilizing collected time-series power consumption data, our study contributes to enhancing power predictions for Docker containers within cloud infrastructures. Our prediction results focus on four Docker containers, each running multiple applications as Docker subprocesses. Power data for individual applications was aggregated to determine total container power consumption. Comparing the performance of ARIMA, ETS, and AR algorithms in predicting Docker container instance power, we found varying outcomes across containers. Through assessing MAPE across different time series model window lengths, we identified superior performance among the models. Specifically, ETS consistently demonstrated the lowest MAPE values for containers like 'polinx-container' and 'alpines-container', indicating higher prediction accuracy compared to ARIMA and AR models. The ARIMA model outperformed the ETS and AR models for the 'progrium container'. These findings underscore the necessity of selecting appropriate time series models tailored to specific Docker container configurations and workload scenarios for precise energy consumption forecasts.INDEX TERMS Docker container; timer series analysis, energy consumption; cloud computing; monitoring.

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Efficient Privacy-Preserving Spatial Data Query in Cloud Computing

Cited by 16 publications

References 41 publications

A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds

A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

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