Focus
Data mining
Turning complex social, productivity, and behavioral data into actionable knowledge.
Data Mining · Machine Learning · AI · PETs
Transforming Data
into Intelligence
The Data Intelligence & Privacy Lab conducts interdisciplinary research in data mining, machine learning, artificial intelligence, and privacy-enhancing technologies.
Method
Models + measurement
Empirical studies, machine learning methods, and evaluation pipelines for real-world data.
Mission
Privacy-aware systems
Responsible analytics with privacy-enhancing technologies and trustworthy AI practices.
About the Lab
Learn moreA research group transforming complex data into useful, responsible intelligence.
The Data Intelligence & Privacy Lab investigates how social, productivity, and behavioral data can be mined, modeled, and interpreted to improve human-centered systems. Our work combines data mining, machine learning, artificial intelligence, and privacy-enhancing technologies.
The lab’s goal is to produce research that is methodologically rigorous, practically useful, and respectful of privacy in data-rich environments.
Research principles
Study real-world data patterns with careful measurement and interpretation.
Design machine learning methods that support human productivity and decision-making.
Advance privacy-preserving approaches for responsible data analysis.
Professor and Members
View allA focused team working across data mining, machine learning, and privacy.
Prof. Yongdae An
Assistant Professor, School of Global Media
Soongsil University
Prof. Yongdae An conducts research in data mining, AI, and privacy-enhancing technologies, focusing on intelligent and trustworthy systems.
Research Interests
Data Mining · Artificial Intelligence · Machine Learning · Privacy-Enhancing Technologies · Trustworthy AI
Lab members
Ph.D. Hanyul Ryu
Researching privacy-enhancing technologies, machine learning, and AI, with a focus on building secure and trustworthy intelligent systems.
M.S. Eungab Jo
Exploring trustworthy and secure AI systems at the intersection of AI, software security, and cyber threat intelligence.
Undergraduate Researcher Sunwoo Ha
Researching interpretable, human-centered intelligent systems at the intersection of HCI and data science.
Incoming
Incoming M.S. · Incoming Student A · Spring 2027
Incoming M.S. · Incoming Student B · Spring 2027
Prospective students with strong data science, machine learning, or privacy backgrounds are encouraged to get in touch.
Research Areas
View allFour themes, one data mining perspective.
Each area connects data, models, privacy, and deployable research practice.
Social & Productivity Data Mining
Investigating patterns and extracting meaningful insights from large-scale social and productivity datasets.
Machine Learning
Developing robust machine learning models for prediction, representation learning, and data-driven decision-making.
Artificial Intelligence
Building intelligent systems for reasoning, automation, and human-centered AI applications.
Privacy-Enhancing Technologies
Designing privacy-preserving technologies for secure computation, trustworthy analytics, and responsible AI systems.
Research Projects
View allResearch projects connecting ideas to real-world impact.
Active Project
Development of a High-Assurance Platform for Sharing and Trading AI Data and Models
Building a trustworthy platform that lets organizations share and trade AI training data and models without exposing the underlying assets. The work covers provenance and integrity verification, privacy-preserving quality and value assessment, usage control over distributed models, and the governance mechanisms needed for a reliable data and model marketplace.
2026 - Present · Data Intelligence & Privacy Lab · Ongoing
Active Project
Development of a Hyper-Personalized Media Recommendation AI Agent Based on On-Device Federated Learning with Privacy-Enhanced Context Matrix Computation
Designing a recommendation agent that keeps personal viewing behavior on the user's device. Federated learning trains a shared media recommendation model without collecting raw logs, while privacy-enhanced context matrix computation combines user, content, and situational signals in protected form, enabling hyper-personalized recommendations under strong privacy guarantees.
2026 - Present · Data Intelligence & Privacy Lab · Ongoing
Selected Publications
View allRecent work from the lab.
2024
Adaptive Successive Over-Relaxation Method for a Faster Iterative Approximation of Homomorphic Operations
Jungho Moon, Zhanibek Omarov, Donghoon Yoo, Yongdae An, Heewon Chung*
FHE.org Submitted2024
Privacy-Enhancing Technologies and Use Cases for Secure AI Development
Yongdae An*
Korean Society of Medical Informatics2021
Privacy-Oriented Technique for COVID-19 Contact Tracing (PROTECT) Using Homomorphic Encryption: Design and Development Study
Yongdae An, Seungmyung Lee, Seungwoo Jung, Howard Park, Yongsoo Song*, Taehoon Ko*
Journal of Medical Internet Research
News
View allLab updates and announcements.
Aug 2026
Data Science course selected as an outstanding EL+ course for Spring 2026.
The lab's Data Science course was recognized as an outstanding EL+ (Experiential Learning Plus) course for the Spring 2026 semester at Soongsil University, in recognition of its project-based curriculum that connects classroom methods with real-world data problems.
Jun 2026
New project on productivity data mining begins.
The lab is developing methods to analyze collaboration patterns and productivity signals in real-world work data.
Read moreMay 2026
Prof. Yongdae An appointed to the 7th Special Review Committee on Artificial Intelligence of the Public Institutional Review Board.
Prof. Yongdae An has been appointed as a review member of the 7th Special Review Committee, the committee dedicated to artificial intelligence research, at the Public Institutional Review Board of Korea. The committee reviews the ethical and privacy implications of AI research involving human subjects and human-derived data.
May 2026
Paper accepted on privacy-preserving machine learning.
Our study explores model training strategies that protect sensitive behavioral and productivity data.
Apr 2026
Recruiting graduate researchers for Fall 2026.
Applicants interested in data mining, machine learning, AI, and privacy-enhancing technologies are welcome.
Contact
Collaborate with us on data mining, AI, and privacy-enhancing technologies.
We welcome research collaborations, student inquiries, and conversations with teams working on data-rich intelligent systems.
Data Intelligence & Privacy Lab
yongdae.an@ssu.ac.krRoom 622, Information Science Building Global Media, Soongsil UniversityProspective students: include a CV, transcript, and a short note on research interests.
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