报告题目1： Studies on UGC Mining Toward Sightseeing 2.0
Qiang Ma, received his Ph.D. Degree from Graduate School of Informatics, Kyoto University in 2004. He was a research fellow (DC2) of JSPS from 2003 to 2004. He joined National Institute of Information and Communication Technology as an expert research in 2004. From 2006 to 2007, he served as an assistant manager at NEC. From October 2007, he joined Kyoto University as an assistant professor, and has been an associate professor since August 2010. His general research interests are in the area of multimedia information systems, data mining and social informatics. His current interest include sightseeing informatics, investment informatics, and Informational Nutrition (information complementation).
Tourism is one of the most important industry in the 21st century. In our group, we are studying information technology to overcome overtourism to realize the sustainable tourism society. In this talk, I will introduce our research activities on UGC (User Generated Content) mining for sightseeing knowledge discovery and its applications. Especially, I will talk about the research about user modeling, quality estimation of sightseeing spots, and tour recommendation.
报告题目2：From Location Privacy to Spatiotemporal Event Privacy
Yang Cao, received his Ph.D. Degree from Graduate School of Informatics, Kyoto University in 2017. He was a postdoctoral fellow at Emory University from 2017 to 2018. He is currently an assistant professor at the department of social informatics, Kyoto University. His research interests includes differential privacy, blockchain, personal data market, and spatiotemporal data management. His works appear in top journals and conferences, including TKDE, ICDE and VLDB. He is also serving as a program committee member in major database conferences including DASFAA, ICDE and SIGSPATIAL.
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users’ location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user’s perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user’s spatiotemporal activities, such as “visited hospital in the last week” or “regularly commuting between Address 1 and Address 2” (it is easy to infer that Addresses 1 and 2 may be home and ofﬁce), which we call it spatiotemporal event. In this talk, I will ﬁrst formally deﬁne spatiotemporal event as Boolean expressions between location and time predicates, and then we can define -spatiotemporal event privacy by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user’s initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with any prior knowledge. I will also demonstrate the effectiveness and efficiency of the proposed method.