Our research aims to formalize the conceptual notion of privacy.
We think of a privacy-jeopardizing-mechanism as a process of signal publication according to probability distribution which is determined by the value of a secret. Consider any two privacy-jeopardizing-mechanisms: it is natural to ask which mechanism is preferable in the context of privacy. We follow a Decision Theory flavored approach and capture the natural preference in such situations by listing some ordinal axioms. These axioms apply a preference relation model. We prove that the common f-divergence represents this relation.
We also follow the reverse direction of the axiomatization approach in order to characterize differential-privacy, which is an ad-hoc standard in Computer Science literature. The preference relation model, which is represented by differential-privacy, demonstrates some properties which are not natural in our eyes.
Our study leads to recommendation on measuring privacy loss by f-divergence functions, such as KL-divergence or Hellinger-distance.
Joint work with Prof. Rann Smorodinsky and Prof. Kobbi Nissim.
Dr. Gail Gilboa-Freedma received her BA degree in Mathematics and Computer Science (cum laude) from the Technion in 2001 and her M.Sc. in Applied Mathematics from the Technion in 2005. She received her Ph.D. from the department of Statistics and Operations Research in the school of Mathematical Sciences in Tel Aviv University in 2011.
Over the years she has worked for Elbit Systems, IBM Research Lab, and the Innovation Center of Citi Group. In 2014-15, she was a Postdoctoral Research Fellow at the faculty of Industrial Engineering and Management in the Technion. She is currently a Postdoctoral Research Fellow at the School of Computer Science in Tel Aviv University and at the Dept. of Electrical Engineering in Columbia University.