Keynote: Mining Behavioral Data: Inference Threats and Privacy Countermeasures
Online services routinely mine behavioral data to perform tasks crucial to their daily operations, including targeted advertising and personalized recommendations. This practice has raised privacy concerns among consumer advocacy groups, regulatory bodies, and the public at large. We study threats arising from the unfettered access to behavioral data, and show that personal information can be successfully inferred from seemingly innocuous online behavior. We also show that behavioral data by multiple users sharing a single online account can be identified as such and separated, establishing that the above threats are relevant even in the presence of multiple users. Finally, we present methods mitigating privacy risks while preserving the utility of services mining behavioral data, using both statistical and cryptographic techniques.
About Stratis Ioannidis:
Stratis Ioannidis is an assistant professor in the ECE Department of Northeastern University, in Boston, MA. He received his B.Sc. (2002) in Electrical and Computer Engineering from the National Technical University of Athens, Greece, and his M.Sc. (2004) and Ph.D. (2009) in Computer Science from the University of Toronto, Canada. Prior to joining Northeastern, he was a research scientist at the Technicolor research centers in Paris, France, and Palo Alto, CA, as well as at Yahoo Labs in Sunnyvale, CA.