Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

A Cross-layer Monitoring Solution based on Quality Models

In order to implement cross-organizational workflows and to realize collaborations between small and medium enterprises (SMEs), the use ofWeb service technology, Service-Oriented Architecture and Infrastructure-as-a- Service (IaaS) has become a necessity. Based on these technologies, the need for monitoring the quality of (a) the acquired resources, (b) the services offered to the final users and (c) the workflow-based procedures used by SMEs in order to use services, has come to the fore. To tackle this need, we propose four metric Quality Models that cover quality terms for the Workflow, Service and Infrastructure layers and an additional one for expressing the equality and inter-dependency relations between the previous ones. To support these models we have implemented a cross-layer monitoring system, whose main advantages are the layer-specific metric aggregators and an event pattern discoverer for processing the monitoring log. Our evaluation is based on the performance and accuracy aspects of the proposed cross-layer monitoring system

Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

the role of memory under the covid-19 outbreak

Alvarenga, M. Z., Oliveira, M. P. V. D., & Oliveira, T. A. G. F. D. (2023). The impact of using digital technologies on supply chain resilience and robustness: the role of memory under the covid-19 outbreak. Supply Chain Management. https://doi.org/10.1108/SCM-06-2022-0217 --- Funding Information: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) Finance Code 001. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project – UIDB/04152/2020 – Centro de Investigação em Gestão de Informação (MagIC).Purpose: This paper’s main aim is to check the mediating effect of supply chain memory in the relationship between using digital technologies and both supply chain resilience and robustness. In addition, the impact of the COVID-19 disruption was tested as a moderator of the impact of supply chain memory on supply chain resilience and robustness. Design/methodology/approach: Altogether, 257 supply chain managers answered the questionnaire, and data were analysed through structural equation modelling. Findings: This paper contributes to theory and practice by demonstrating that the experience, familiarity and knowledge to deal with disruptions partially mediate the relationship between digital technologies, resilience and robustness. Moreover, our results show that memory is less efficient for the supply chain to maintain an acceptable level of performance in case of a new extreme disruptive event like COVID-19. The full model was able to explain 36.90% of supply chain memory, 41.58% of supply chain resilience and 46.21% of supply chain robustness. Originality/value: The study helps to understand how to develop supply chain memory, positioning digital technologies as an antecedent of it. The impact of supply chain memory on supply chain resilience and robustness is proved. Knowledge about the impact of industry 4.0 technologies on disruption management is quantitatively improved. It demonstrates that digital technologies impact resilience and robustness mainly through supply chain memory. The study proves that supply chain memory is less efficient for the chain remains effective when a non-routine disruptive event occurs, but it is still imperative to recover from it.authorsversionepub_ahead_of_prin

Intent-based zero-touch service chaining layer for software-defined edge cloud networks

Edge Computing, along with Software Defined Networking and Network Function Virtualization, are causing network infrastructures to become as distributed clouds extended to the edge with services provided as dynamically established sequences of virtualized functions (i.e., dynamic service chains) thereby elastically addressing different processing requirements of application data flows. However, service operators and application developers are not inclined to deal with descriptive configuration directives to establish and operate services, especially in case of service chains. Intent-based Networking is emerging as a novel approach that simplifies network management and automates the implementation of network operations required by applications. This paper presents an intent-based zero-touch service chaining layer that provides the programmable provision of service chain paths in edge cloud networks. In addition to the dynamic and elastic deployment of data delivery services, the intent-based layer offers an automated adaptation of the service chains paths according to the application's goals expressed in the intent to recover from sudden congestion events in the SDN network. Experiments have been carried out in an emulated network environment to show the feasibility of the approach and to evaluate the performance of the intent layer in terms of network resource usage and adaptation overhead

ENABLING MOBILE DEVICES TO HOST CONSUMERS AND PROVIDERS OF RESTFUL WEB SERVICES

The strong growth in the use of mobile devices such as smartphones and tablets in Enterprise Information Systems has led to growing research in the area of mobile Web services. Web services are applications that are developed based on network standards such as Services Oriented Architecture and Representational State Transfer (REST). The mobile research community mostly focused on facilitating the mobile devices as client consumers especially in heterogeneous Web services. However, with the advancement in mobile device capabilities in terms of processing power and storage, this thesis seeks to utilize these devices as hosts of REST Web services. In order to host services on mobile devices, some key challenges have to be addressed. Since data and services accessibility is facilitated by the mobile devices which communicate via unstable wireless networks, the challenges of network latency and synchronization of data (i.e. the Web resources) among the mobile participants must be addressed. To address these challenges, this thesis proposes a cloud-based middleware that enables reliable communication between the mobile hosts in unreliable Wi-Fi networks. The middleware employs techniques such as message routing and Web resources state changes detection in order to push data to the mobile participants in real time. Additionally, to ensure high availability of data, the proposed middleware has a cache component which stores the replicas of the mobile hosts’ Web resources. As a result, in case a mobile host is disconnected, the Web resources of the host can be accessed on the middleware. The key contributions of this thesis are the identification of mobile devices as hosts of RESTful Web services and the implementation of middleware frameworks that support mobile communication in unreliable networks

Towards smart city models: evaluation of methods and performance indexes for the smart urban contexts development

Today, cities are facing many challenges such as pollution, resource consumption, gas emissions and social inequality. Many future city views have been developed to solve these issues such as the Smart City model. In literature several methods have been proposed to plan a Smart city, but, only a few of them have been really applied to the urban context. Most of them are indeed theoretical and qualitative approaches, providing scenarios that have not been applied to real universities campus/cities/districts. In this framework, the aim of this thesis is to integrate a previous qualitative smart method and transform it into a quantitative and ex-post one. The feasibility and validity of the method will be tested through the comparison with another existing model and the application of both approaches on two real case studies, characterized by different territorial levels. Finally, the flexibility of this new quantitative smart methodology is demonstrated throughout its application on another two urban contexts: highland villages and the Italian suburb. Results of the analysis show that this smart method is reliable and provide coherent results, becoming a useful instrument for designers and planners for the identification of the most performing Smart strategies