应网络与交换技术国家重点实验室程祥副教授的邀请，卡塔尔计算机研究所Ting Yu教授将于7月11日来北京邮电大学作学术报告。欢迎校内广大师生踊跃参加。
讲座题目：Heavy hitter estimation over set-valued data with local differential privacy
主讲人：Ting Yu（于挺）教授
主持人：程祥
时 间：2017年7月11日（星期二）上午9:30—11:30
地 点：新科研楼510会议室
Abstract：
In local differential privacy (LDP), each user perturbs her data locally before sending them to an untrusted data collector, who analyzes the data to obtain useful statistics. Unlike the setting of traditional differential privacy, in LDP data collectors never gain access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collectors themselves against liability if data leakage happens. This different setting requires rethinking of techniques to perform various data analysis tasks.
In this talk, we present a systematic study of heavy hitter mining over set-valued data under LDP. We first review existing solutions, extend them to the heavy hitter estimation, and explain why their effectiveness is limited. We then propose LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset. We provide both in-depth theoretical analysis and extensive experiments to compare LDPMiner against adaptations of previous solutions. The results show that LDPMiner significantly improves over existing methods. More importantly LDPMiner successfully identifies the majority true heavy hitters in practical settings.
Bio：
Ting Yu is a principal scientist in the cyber security group of Qatar Computer Research Institute (QCRI), and joint Professor in the College of Science and Engineering, Hamad Bin Khalifa University. Before joining QCRI in 2013, he was an associate professor in the faculty of Computer Science Department, North Carolina State University. He obtained his BS from Peking University in 1997, MS from Minnesota University in 1998, and PhD from the University of Illinois at Urbana-Champaign in 2003, all in computer science. He is a recipient of the NSF CAREER Award in 2007. His research areas focus on privacy preserving data analysis, data anonymization, and security analytics.
