✔ Privacy-Preserving Data Collection

● LDP (Local Differential Privacy) is the state-of-the-art approach to protect individual privacy in the process of data collection. The goal of our research is to develop the techniques to collect sensitive personal data from various IoT devices, such as smartphones, smartwatches and wearable health devices, in a privacy-preserving manner.

✔ Privacy-preserving data publishing (PPDP)

● Privacy-preserving data publishing provides methods and tools for publishing useful information while preserving data privacy. Our research is focused on developing efficient PPDP algorithms in a distributed environment, such as Hadoop or Spark distributed framework.

하둡 분산 환경 기반 k 익명화 시스템

✔ Secure Query Processing in a Cloud Environment

● Cloud-based data outsourcing solutions pose many security challenges, because the users' sensitive data are stored within the public cloud servers, which are very likely to reside outside of the trusted domain of the users.

● Our research is focused on the development of secure query processing algorithms in a cloud environment.

✔ Big Data Aanalytics