Building robust m-commerce payment system on offline wireless network

Mobile commerce is one of the upcoming research area with focus on mobile payment systems. Unfortunately, the current payment systems is directly dependent on fixed infrastructure of network (cellular network), which fails to facilitate optimal level of security for the payment system. The proposed system highlights a novel approach for building a secure, scalable, and flexible e-payment systems in the distributed scenario of wireless adhoc network in offline mode of communication for enhanced security on transaction and payment process. The proposed system uses Simple Public Key Infrastructure for providing the security in payment processes. The performance analysis of the proposed model shows that the system is highly robust and secure ensuring anonymity, privacy, non-repudiation offline payment system over wireless adhoc network

A generic framework for process execution and secure multi-party transaction authorization

Process execution engines are not only an integral part of workflow and business process management systems but are increasingly used to build process-driven applications. In other words, they are potentially used in all kinds of software across all application domains. However, contemporary process engines and workflow systems are unsuitable for use in such diverse application scenarios for several reasons. The main shortcomings can be observed in the areas of interoperability, versatility, and programmability. Therefore, this thesis makes a step away from domain specific, monolithic workflow engines towards generic and versatile process runtime frameworks, which enable integration of process technology into all kinds of software. To achieve this, the idea and corresponding architecture of a generic and embeddable process virtual machine (ePVM), which supports defining process flows along the theoretical foundation of communicating extended finite state machines, are presented. The architecture focuses on the core process functionality such as control flow and state management, monitoring, persistence, and communication, while using JavaScript as a process definition language. This approach leads to a very generic yet easily programmable process framework. A fully functional prototype implementation of the proposed framework is provided along with multiple example applications. Despite the fact that business processes are increasingly automated and controlled by information systems, humans are still involved, directly or indirectly, in many of them. Thus, for process flows involving sensitive transactions, a highly secure authorization scheme supporting asynchronous multi-party transaction authorization must be available within process management systems. Therefore, along with the ePVM framework, this thesis presents a novel approach for secure remote multi-party transaction authentication - the zone trusted information channel (ZTIC). The ZTIC approach uniquely combines multiple desirable properties such as the highest level of security, ease-of-use, mobility, remote administration, and smooth integration with existing infrastructures into one device and method. Extensively evaluating both, the ePVM framework and the ZTIC, this thesis shows that ePVM in combination with the ZTIC approach represents a unique and very powerful framework for building workflow systems and process-driven applications including support for secure multi-party transaction authorization

Agile security for web applications

Web-based applications (or more concisely, Web applications) are a kind of information system with a particular architecture. They have progressively evolved from Internet browser-based, read-only information repositories to Web-based distributed systems. Today, increasing numbers of businesses rely on their Web applications. At the same time, Web applications are facing many security challenges and, as a result, are exposing businesses to many risks. This thesis proposes a novel approach to building secure Web applications using agile software development methods

Enhancing climate resilience in buildings using Collective Intelligence: A pilot study on a Norwegian elderly care center

The combined challenge of climate change and population aging requires novel solutions that enhance the resilience of building energy systems and secure indoor comfort for vulnerable occupants in extreme weather conditions. This research investigates the performance of a newly developed Energy Management (EM) system based on Collective Intelligence (CI) and Reinforcement Learning (RL), called CIRLEM, managing the energy performance of an urban complex in Ålesund, Norway, including an elderly care center with decentralized PV generation, EV charging and storage, while connected to a main electricity grid. CIRLEM controls multiple flexibility assets including independent thermal zones (the demand-side agents) and Electric Vehicle (EV) charging stations (the local storage). In a novel approach, CIRLEM coordinates the distributed storage and generation together with the demand side to control energy systems and react collaboratively to environmental variations. Under extreme weather conditions, without applying CIRLEM, the demand can be more than double that of typical weather conditions. The implementation of the double-layer CIRLEM can reduce the total demand by 35 % over a month. Furthermore, the inclusion of photovoltaic (PV) systems allows the system to be independent from the grid for almost 40 % of its operational hours, while adding EV storage can increase it to around 70 %. Finally, the application of CIRLEM reduced overheating hours from 17 h ∙°C to 2 h ∙°C under extreme conditions, while maintaining comfortable conditions even during temperature ramps

State of The Art and Hot Aspects in Cloud Data Storage Security

Along with the evolution of cloud computing and cloud storage towards matu- rity, researchers have analyzed an increasing range of cloud computing security aspects, data security being an important topic in this area. In this paper, we examine the state of the art in cloud storage security through an overview of selected peer reviewed publications. We address the question of defining cloud storage security and its different aspects, as well as enumerate the main vec- tors of attack on cloud storage. The reviewed papers present techniques for key management and controlled disclosure of encrypted data in cloud storage, while novel ideas regarding secure operations on encrypted data and methods for pro- tection of data in fully virtualized environments provide a glimpse of the toolbox available for securing cloud storage. Finally, new challenges such as emergent government regulation call for solutions to problems that did not receive enough attention in earlier stages of cloud computing, such as for example geographical location of data. The methods presented in the papers selected for this review represent only a small fraction of the wide research effort within cloud storage security. Nevertheless, they serve as an indication of the diversity of problems that are being addressed

Managed ecosystems of networked objects

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things

Interoperability, Trust Based Information Sharing Protocol and Security: Digital Government Key Issues

Improved interoperability between public and private organizations is of key significance to make digital government newest triumphant. Digital Government interoperability, information sharing protocol and security are measured the key issue for achieving a refined stage of digital government. Flawless interoperability is essential to share the information between diverse and merely dispersed organisations in several network environments by using computer based tools. Digital government must ensure security for its information systems, including computers and networks for providing better service to the citizens. Governments around the world are increasingly revolving to information sharing and integration for solving problems in programs and policy areas. Evils of global worry such as syndrome discovery and manage, terror campaign, immigration and border control, prohibited drug trafficking, and more demand information sharing, harmonization and cooperation amid government agencies within a country and across national borders. A number of daunting challenges survive to the progress of an efficient information sharing protocol. A secure and trusted information-sharing protocol is required to enable users to interact and share information easily and perfectly across many diverse networks and databases globally.Comment: 20 page

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare