Society benefits from the exchange of large-scale data in many ways. Anonymization is the usual mechanism for addressing the privacy of data subjects. Unfortunately, anonymization is broken.
Oct 5, 2016 Inside Sources
Osonde Osoba (pronounced “oh-shOwn-day aw-shAw-bah”) is an engineer at the RAND Corporation and a professor at the Pardee RAND Graduate School. He has a background in the design and optimization of machine learning algorithms. He has applied his machine learning expertise to diverse policy areas such as health, defense, and technology policy. His more recent focus has been on data privacy and accountability in algorithmic systems and artificial intelligence.
Prior to joining RAND, he was a researcher at the University of Southern California (USC). His research there focused on improving the speed and robustness of popular statistical algorithms like the expectation-maximization (EM) and backpropagation algorithms used in applications like automatic speech recognition. He also made contributions on the robustness and accuracy of approximate Bayesian inference schemes. Osoba received his Ph.D. in electrical engineering from USC and his B.S. in electrical and computer engineering from University of Rochester.
Osonde A. Osoba, Bart Kosko, "Fuzzy Cognitive Maps of Public Support for Insurgency and Terrorism," Journal of Defense Modeling and Simulation, 14(1), 2017
K. Audhkhasi, O. Osoba, and B. Kosko, "Noise-Enhanced Convolutional Neural Networks," Neural Networks, 78(0), 2016
Osonde Osoba, Bart Kosko, "Noise-enhanced clustering and competitive learning algorithms," Neural Networks, 37, 2013
Osonde Osoba, Sanya Mitaim, and Bart Kosko., "The noisy expectation–maximization algorithm," Fluctuation and Noise Letters, 12(03), 2013