Dr. Hafiz Imtiaz

Developed a novel and robust protocol for decentralized differentially private computations that are common in signal processing and machine learning in decentralized data settings. The protocol achieves the same privacy-utility trade-off in the decentralized data setting by employing a correlated noise scheme. We show the effectiveness of the scheme on a decentralized independent component analysis problem on real and synthetic neuroimaging data. This work led to this IEEE Transactions on Signal Processing publication.