Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Modeling Natural Hazards Engineering Data to Cyberinfrastructure

DesignSafe-CI is an end-to-end data lifecycle management, analysis, and publication cloud platform for natural hazards engineering. To facilitate ongoing data curation and sharing in a cloud environment that is intuitive to the end users, developers and curators teamed with experts in the different hazards to design data models and vocabularies that map their research workflows and domain terminology. The experimental data models - six - emphasize provenance through relationships between research processes, data and their documentation, and highlight commonalities between experiment types. They mediate between the user interface and the repository layers of the cyberinfrastructure to automate tasks such as organizing data and facilitating its description. Using data from triaxial experiments, we conducted a user evaluation of the geotechnical data model, both for its fitness to real data and for purposes of data understandability during reuse. The results of the evaluation guided testing and selection of the Fedora 4 repository backend to enhance data discovery and reuse.National Science FoundationTexas Advanced Computing Center (TACC

Twos Company, Threes A Cloud: Challenges To Implementing Service Models

Although three models are currently being used in cloud computing (Software as a Service, Platform as a Service, and infrastructure as a service, there remain many challenges before most business accept cloud computing as a reality. Virtualization in cloud computing has many advantages but carries a penalty because of state configurations, kernel drivers, and user interface environments. In addition, many non-standard architectures exist to power cloud models that are often incompatible. Another issue is adequately provisioning the resources required for a multi-tier cloud-based application in such a way that on-demand elasticity is present at vastly different scales yet is carried out efficiently. For networks that have large geographical footprints another problem arises from bottlenecks between elements supporting virtual machines and their control. While many solutions have been proposed to alleviate these problems, some of which are already commercial, much remains to be done to see whether these solutions will be practicable at scale up and address business concerns

Minimalist Architecture to Generate Embedded System Web User Interfaces

Part 9: Embedded Systems and Petri NetsInternational audienceThis paper presents a new architecture to semi-automatically generate Web user interfaces for Embedded Systems designed using IOPT Petri Net models. The user interfaces can be used to remotely control, monitor and debug embedded systems using a standard Web Browser. The proposed architecture takes advantage of the distributed nature of the Internet to store all static user interface data and software on third-party Web services (the Cloud), and execute the user-interface code on the user’s Web Browser. A simplified protocol is proposed to enable remote control, status-monitoring, debugging and step-by-step execution, minimizing resource consumption on the physical embedded devices, including processing load, memory and communication bandwidth. As the user interface data and code are kept on third-party Web services, these resources can be shared among multiple embedded device units, and the hardware requirements to implement the devices can be simplified, leading to reduced cost solutions. To prevent down-time due to network problems or server failures, a fault-tolerant topology is suggested. The distributed architecture is transparent to end-users, observing just a Web interface for an embedded device on the other side of an Internet URL

Real estate project delivery system

Organizations that have real estate holdings often spend substantial amounts on development of real estate for office spaces, production facilities, data centers, etc. Such organizations typically rely on third-party software solutions and consultants to manually track real-estate project costs and schedules. The present techniques provide a cloud based project delivery system that integrates the organization’s real-estate management applications with cloud based applications and building information models. With such integration, the techniques enable monitoring of project costs and schedules using a flexible user interface. The techniques can extract insights by integrating project cost, schedule, and attribute data with building information models and provide analytics based on such data. The techniques also learn from real-estate project history to further enhance the project delivery system