Data Mining for Privacy Protection, Authorship Analysis, and Malware Analysis
Dr. Benjamin C. M. Fung
Canada Research Chair & Associate Professor
School of Information Studies
1530-1630, 09 Oct 2017
SCT909, Cha Chi Ming Science Tower, Ho Sin Hang Campus
The objective of this presentation is to provide an overview of the research work conducted in the McGill Data Mining and Security (DMaS) Lab. We will discuss three research topics. (1) Privacy-preserving data mining: Since data mining often involves person-specific and sensitive information, the public has acquired the negative impression that data mining is a tool for intrusion on their privacy. Privacy-preserving data mining is a study of eliminating threats to privacy while preserving useful information in the released data. (2) Authorship analysis: Given an anonymous e-mail or some tweets, can we identify the author or infer the author's characteristics based on his/her writing styles? I will give a live software demonstration on visualizing the writing style of a person. (3) Assembly code mining: Assembly code analysis is one of the critical processes for mitigating the exponentially increasing threats from malicious software. However, it is a manually intensive and time-consuming process even for experienced reverse engineers. An effective and efficient assembly code clone search engine can greatly reduce the effort of this process. We have implemented an award winning assembly clone search engine called Kam1n0. It is the first clone search engine that can efficiently identify a given query assembly function's subgraph clones from a large assembly code repository. I will give a live demonstration of Kam1n0.
Dr. Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, an Associate Professor of School of Information Studies (SIS) at McGill University, and a Co-curator of Cybersecurity in the World Economic Forum (WEF). Collaborating closely with the national defense, law enforcement, transportation, and healthcare sectors, he has published over 100 refereed articles that span across the research forums of data mining, privacy protection, cyber forensics, services computing, and building engineering. His data mining works in crime investigation and authorship analysis have been reported by media worldwide, including New York Times, BBC, CBC, etc. Before joining McGill, he was an Assistant/Associate Professor at Concordia University, and a system software developer at SAP in Canada. Dr. Fung is a licensed professional engineer in software engineering. See his research website http://dmas.lab.mcgill.ca/fung for more information.