Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Exploring the Frontiers of Feasible Computation. The project aims to delineate the boundary between feasible and infeasible computational problems. A problem is considered feasible if there is an algorithm to solve it in worst-case time bounded by a polynomial in the input size. This is probably impossible for the important class of NP-complete problems. However, typical examples of NP-complete problems can often be solved in polynomial time, because worst-case problems are rare. The project is ....Exploring the Frontiers of Feasible Computation. The project aims to delineate the boundary between feasible and infeasible computational problems. A problem is considered feasible if there is an algorithm to solve it in worst-case time bounded by a polynomial in the input size. This is probably impossible for the important class of NP-complete problems. However, typical examples of NP-complete problems can often be solved in polynomial time, because worst-case problems are rare. The project is relevant to public-key cryptography, where breaking an encryption scheme should be infeasible, and to many real-life situations where NP-complete problems need to be solved, either exactly or approximately.Read moreRead less