E-government / E-democracy

In what ways does the web change the ways we interact with government and change the ways we engage in politics?

Open Government Data

Linked Open data – a platform for modern science, engineering, education and business. In the more recent talk, Sir Nigel Shadbolt speaks about "The Value of Openess - The Open Data Institute and Publically Funded Open Data" during the Natural History Museum of London Informatics Horizons event.

WAIS Seminar:Secure Multi-Party Computation for the Masses

Speaker(s): Prof. David Evans Organiser: Dr Tim Chown Time: 22/05/2014 10:45-11:45 Location: B53/4025 Abstract Secure multi-party computation enables two (or more) participants to reliably compute a function that depends on both of their inputs, without revealing those inputs to the other party or needing to trust any other party. It could enable two people who meet at a conference to learn who they known in common without revealing any of their other contacts, or allow a pharmaceutical company to determine the correct dosage of a medication based on a patient’s genome without compromising the privacy of the patient. A general solution to this problem has been known since Yao's pioneering work in the 1980s, but only recently has it become conceivable to use this approach in practice. Over the past few years, my research group has worked towards making secure computation practical for real applications. In this talk, I'll provide a brief introduction to secure computation protocols, describe the techniques we have developed to design scalable and efficient protocols, and share some recent results on improving efficiency and how secure computing applications are developed.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.