Yuan Hong

Illinois Institute of Technology


Lecture Information:
  • November 30, 2018
  • 1:00 PM
  • ECS 241
Photo of Yuan Hong

Speaker Bio

Yuan Hong is an Assistant Professor in the Department of Computer Science at Illinois Institute of Technology. He received his Ph.D. degree from Rutgers, the State University of New Jersey. His research interests primarily lie in the fields of privacy, security, optimization and data analytics, and especially at their intersection. As such, he is very interested in resolving the security and privacy issues in both fundamental problems (e.g., optimization models) and data intensive systems (e.g., smart grid, network monitoring, and search engine). His publications have appeared in CCS, TDSC, TIFS, TEM, CIKM, EDBT, ICDM, etc. His research has been supported by the NSF, and he is a Senior Member of IEEE.

Description

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix- preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this talk, I will present a novel network trace anonymization scheme that preserves both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility.