In privacy-preserving data collection, each user perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. Unlike the setting of well-known differential privacy, in Local Differential Privacy (LDP) the data collector never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. No existing work tackles more complex data mining tasks such as heavy hitter discovery over set-valued data. In this talk, I will introduce the challenges of heavy hitter mining under LDP, then present my recent work LDPMiner, a two-phase mechanism for obtaining accurate heavy hitters with LDP. In addition, my previous works on secure cloud computing and other ongoing research projects will also be briefly introduced.
Zhan Qin is a PhD candidate in the Department of Computer Science and Engineering at State University of New York at Buffalo. Zhan's research enables privacy-preserving data collection, computation and publication. He explores and develops novel security sensitive algorithms and protocols for computation and communication on IoT devices.He was named the Best Graduate Research Award in CSE for 2016 by the Department of CSE at the University at Buffalo.