报告题目：On-the-fly Privacy for Location Histograms
报 告 人：Kaitai Liang
工作单位：Delft University of Technology
Dr. Kaitai Liang joined the Cybersecurity group at Delft University of Technology in 2020. Before joining TU Delft, he was an Assistant Professor in Secure Systems at the University of Surrey, UK, and an academic member of the Surrey Centre for Cyber Security. He received his PhD degree in computer science from City University of Hong Kong. His main focus is on the design and implementation of cryptographic protocols to security. He has involved (as PI and CI) in several European funded projects and H2020 proposals, e.g., IRIS, ASSURED, SPEAR, SECONDO, CUREX, Academic of Finland, and delivered real-world impact via these projects with his academic (e.g., University of Birmingham, University of Luxemburg) and industrial partners (e.g., IBM, Huawei Germany, Infineon). He has also maintained a tight and strong research relationships with Europe, Asia-pacific and northern America academic communities. He has published a series of research works (over 80 publications, more than 2,600 citations), applying information security and crypto tools to tackle real-world problems, in many high tier international journals and conferences (>10 A* and >10 A publications in the past 5 years), e.g., IEEE TIFS, IEEE NETWORK, IEEE Transactions on Industrial Informatics, ESORICS. He has served as technical program committee for over 20 renowned international security/privacy conferences, e.g., ESORICS, ACNS, TRUSTCOM, ASIACCS. He has contributed to ISO standard being an official ISO member of ISO Crypto Sub Committee IST/33/2. In addition to being active reviewers for many international journals/conferences, he also serves as associate editor, e.g., for the Computer Journal, guest editor, e.g., for IEEE Transactions on industrial informatics, and cybersecurity consultant for SMEs.
An important motivation for research in location privacy has been to protect against user profiling, i.e., inferring a user’s political affiliation, wealth level, sexual preferences, religious beliefs and other sensitive attributes. Existing approaches focus on distorting or suppressing individual locations, but we argue that, for directly protecting against profiling, it is more appropriate to focus on the frequency with which various locations are visited – in other words, the histogram of a user’s locations. We introduce and explore a new privacy notion, namely, on-the-fly privacy for location histograms, in which a mobile user repeatedly submits obfuscated locations to a Location-Based Service aiming for the resulting histogram to resemble a target profile or differ from it. For example, she may want to avoid looking wealthy or to resemble a health conscious person. We describe how to design concrete privacy mechanisms that operate under different assumptions on, e.g. the user’s mobility, including provably optimal mechanisms. We use a mobility dataset with 1083 users to illustrate how these mechanisms achieve privacy while minimizing the quality loss caused by the location obfuscation, in the context of two types of Location-Based Services: nearest-PoI, and geofence.