Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.

Sharing in the Rain: Secure and Efficient Data Sharing for the Cloud

Cloud storage has rapidly become a cornerstone of many businesses and has moved from an early adopters stage to an early majority, where we typically see explosive deployments. As companies rush to join the cloud revolution, it has become vital to create the necessary tools that will effectively protect users' data from unauthorized access. Nevertheless, sharing data between multiple users' under the same domain in a secure and efficient way is not trivial. In this paper, we propose Sharing in the Rain – a protocol that allows cloud users' to securely share their data based on predefined policies. The proposed protocol is based on Attribute-Based Encryption (ABE) and allows users' to encrypt data based on certain policies and attributes. Moreover, we use a Key-Policy Attribute-Based technique through which access revocation is optimized. More precisely, we show how to securely and efficiently remove access to a file, for a certain user that is misbehaving or is no longer part of a user group, without having to decrypt and re-encrypt the original data with a new key or a new policy.

Secure & Trusted Communication in Emergency Situations

In this paper we propose SETS, a protocol with main aim to provide secure and private communication during emergency situations. SETS achieves security of the exchanged information, attack resilience and user's privacy. In addition, SETS can be easily adapted for mobile devices, since field experimental results show the effectiveness of the protocol on actual smart-phone platforms.

Towards an Application-Specific Thermal Energy Model of Current Processors

Recent developments of high-end processors recognize temperature monitoring and tuning as one of the main challenges towards achieving higher performance given the growing power and temperature constraints. To address this challenge, one needs both suitable thermal energy abstraction and corresponding instrumentation. Our model is based on application-specific parameters such as power consumption, execution time, and asymptotic temperature as well as hardware-specific parameters such as half time for thermal rise or fall. As observed with our out-of-band instrumentation and monitoring infrastructure, the temperature changes follow a relatively slow capacitor-style charge-discharge process. Therefore, we use the lumped thermal model that initiates an exponential process whenever there is a change in processor’s power consumption. Initial experiments with two codes – Firestarter and Nekbone – validate our thermal energy model and demonstrate its use for analyzing and potentially improving the application-specific balance between temperature, power, and performance.

Community-led upgrading for self-reliance in informal settlements in South Africa: a review

Around the world, informal and low-income settlements (so-called “slums”) have been a major issue in city management and environmental sustainability in developing countries. Overall, African cities have an agenda for slum management and response. For example, the South African government introduced the Upgrade of Informal Settlements Program (UISP), as a comprehensive plan for upgrading slum settlements. Nevertheless, upgrading informal settlements from the bottom-up is key to inform broad protocols and strategies for sustainable communities and `adaptive cities´. Community-scale schemes can drive sustainability from the bottom-up and offer opportunities to share lessons learnt at the local level. Key success factors in their roll-out are: systems thinking; empowered local authorities that support decentralised solutions and multidisciplinary collaboration between the involved actors, including the affected local population. This research lies under the umbrella of sustainable bottom-up urban regeneration. As part of a larger project of collaboration between UK and SA research institutions, this paper presents an overview of in-situ participatory upgrade as an incremental strategy for upgrading informal settlements in the context of sustainable and resilient city. The motivation for this research is rooted in identifying the underpinning barriers and enabling drivers for up-scaling community-led, participatory upgrading approaches in informal settlements in the metropolitan area. This review paper seeks to provide some preliminary guidelines and recommendations for an integrated collaborative environmental and construction management framework to enhance community self-reliance. A theoretical approach based on the review of previous studies was combined with a pilot study conducted in Durban (South Africa) to investigate the feasibility of community-led upgrading processes.