PROJECT THEME
Research on Privacy-Preserving Data Analysis Platform for Web and IoT
Accurate data analysis while protecting personal information
Web and IoT technologies have greatly improved the convenience of people. However, privacy leakage due to unexpected combination with unexpected data or presence of sensor data with errors has become a major issue. In this research, we will work on the research and development of a cross-Web/IoT privacy-preserving data analysis infrastructure that can be controlled based on an understanding of privacy risks, and that can perform machine learning and statistical data analysis safely and accurately.
RESEARCHER
PROFILE
University of Electro-Communications
Department of Informatics, Graduate School of Informatics and Engineering
RESEARCH
CONTENT
Summary
The advancement of web and IoT technologies has significantly enhanced people's convenience, and in recent years, there has been an emphasis on promoting the sharing of such Web/IoT data. Concurrently, concerns have arisen regarding privacy breaches due to unanticipated data combinations and the presence of sensor data with errors. In this study, we propose the development of a Web/IoT privacy-preserving data analysis platform that can effectively identify and manage privacy risks while conducting machine learning and statistical data analysis securely and accurately.
Goal
We aim to create software capable of safely processing large quantities of data, even when faced with missing or erroneous information.
Originality
Existing methods ensure privacy protection within the scope of predetermined data. However, this study extends privacy protection to incorporate unanticipated data combinations. While previous privacy-preserving data mining research has focused on accurate data, our investigation targets a diverse array of data types, including those that exhibit significant variations in type, accuracy, and volume, as well as data containing errors.
Challenges
A primary challenge is to address open, unmaintained, and dynamic data, rather than static datasets maintained in a closed environment.
Forward perspective
Future development of research results: Integrating open data reasoning techniques will lead to the exploration of frameworks that proactively collect necessary personal data and present solutions by identifying the problems to be addressed. Relationship between research results and society: The developed platform will be released as open-source software to encourage the development of services that utilize data securely and freely.
ACCESS
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