Future Projects - Institute of Applied Data Analytics

Private web search using query obfuscation

  • Institute of Applied Data Analytics
  • Contact:Shariq Bashir
  • Application invited for:

The aim of the project is to develop a novel Information Retrieval (IR) system, which is a web search facility, that uses our proposed proxy-terms based query obfuscation technique that allows users to search information through proxy queries without submitting true queries, harnessing the high computational and storage power of High Performance Computing (HPC). As part of the IR system architecture to support 1) data collection of large amount of retrieved documents from the web and 2) to support high computational, intelligent IR processing of documents using smart and artificial intelligence technologies, a HPC infrastructure will be incorporated. As a first project, we will focus in the medical domain. Not only will the large amount of data collect enable us to develop novel IR systems, it allows us to do research in the areas of medical web search privacy and use of HPC in medical analytics or big data medical analytics and infrastructure. Search engines store users’ queries in query log. However, query log causes privacy concerns. Private web search (PWS) provides privacy-preserving technique that allows users to retrieve information from IR system without revealing true search queries. Existing techniques achieve web search privacy in an isolated manner without considering similarity between consecutive queries. In this project, we want to propose a proxy-terms based query obfuscation technique that allows users to search information through proxy queries without submitting true queries.

Prediction of wave and tidal in deep-water (ocean engineering)

Grant Details: UBD/RSCH/1.11/FICBF(b)/2020/004 Reliable and accurate predictions of deep-water wave conditions is crucial for any ocean engineering activities, for example, safe ship navigation, design of marine structures (like oil platforms and harbours), and design and management of marine energy systems as it has an impact on human safety, economics and clean energy production. Different empirical, numerical and soft-computing approaches have been proposed for wave height prediction. Wave conditions in regions where historical data is available and collected can be predicted using numerical and soft-computing methods. However, for new regions of interest, in this case Brunei sea, historical data of wave conditions is not available. Hence, there is a need for collecting and predicting the wave conditions data in Brunei sea. This project will employ advanced artificial intelligence (AI) algorithms and modelling methods to improve the accuracy of the wave conditions prediction.