Research
- Privacy-Preserving Computing
I’m working on this project as my ELEC 599 project in Rice U.
In several scenarios, there is a need to match a query against a dataset, where the query/dataset belongs to different parties and each of them requires keeping their own data private. The importance of this requirement arises in many various areas, e.g., medical history and criminal data. A frequent application of privacy-preserving scenario is matching. For example, Alice wants to find if she has a genetic disorder by matching her genome information with Bob’s genetic disorder bank. But she doesn’t want to reveal her private information and so does Bob.This project aims to address the privacy-preserving matching using Yao’s Garbled Circuit (GC) protocol. GC protocol has shown to be the most efficient secure two party computation approach. This protocol allows two parties to evaluate a function which is described as a Boolean circuit on their private data. This project objective is to study the applicability of GC-based privacy preserving protocols on real benchmarks and optimize its performance for real application on embedded or reconfigurable devices.
Abstract & Timetable
- Fast K-Nearest Neighbor Search (KNN)
During the past semester I was working on developing a fast new KNN search and optimize it in order to implement it on real hardware platform.
Presentation on reviewing state-of-art approach (FLANN)
- SSVEP (Steady State Visual Evoked Potential) based BCI (Brain-Computer Interface)
This research was my B.Sc. project and the aim was to create a SSVEP-based BCI for the application of Intelligent Phone-Dialing. You can see the details in Academic Projects section.
