About the Event
In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions.
We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. In particular, I will discuss our Doppelgänger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds. We also have developed a tool, Anonymouth, to help users understand their vulnerability to stylometric analysis and change their writing style.
Rachel Greenstadt is an Assistant Professor of Computer Science at Drexel University, where she research the privacy and security properties of intelligent systems and the economics of electronic privacy and information security. Her work is at "layer 8" of the network—analyzing the content. She is a member of the DARPA Computer Science Study Group and she runs the Privacy, Security, and Automation Laboratory (PSAL) which is a vibrant group of ten researchers. The privacy research community has recognized her scholarship with the PET Award for Outstanding Research in Privacy Enhancing Technologies, the NSF CAREER Award, and the Andreas Pfitzmann Best Student Paper Award.