My current research interests span data privacy, machine learning, information theory, operational data compression, and convex optimization.

Data Privacy (Universitat Politècnica de Catalunya, from 2008 to date)

I am currently a postdoctoral researcher with the Information Security Group at the Technical University of Catalonia, in Barcelona, Spain. 

I collaboration with Prof. Jordi Forné and Prof. Miquel Soriano, we investigate pioneer trends in data-perturbative privacy-enhancing technologies, focusing on database anonymization with high utility preservation, such as medical electronic records for large-scale studies, and any other type of efficient privacy mechanisms for statistical disclosure control.

We combine powerful formalisms from information theory, data compression, machine learning, and convex optimization, to attain an optimal compromise between privacy, on the one hand, and data utility and overall system functionality on the other. 

https://sites.google.com/site/davidrebollomonedero/files/UPC%20-%20Data%20Privacy%20170823a%20%28Double%20Motivation%29.pdf
A conceptual presentation of our research on modern data privacy technologies is available here:
D. Rebollo-Monedero, "New trends in data privacy: A conceptual overview of our research," Presentation, Technical University of Catalonia, Aug. 2017 [PDF]. Section 2 focuses on database anonymization.










Conceptual depiction of our research in modern data-privacy technologies.

Data Compression (Stanford University, until 2008)


My previous research, as a doctoral candidate at Stanford University, in California, USA, under the supervision of Prof. Bernd Girod, dealt with distributed source coding. 

A PDF copy of my PhD dissertation is available here:
D. Rebollo-Monedero, "Quantization and transforms for distributed source coding," Ph.D. dissertation, Stanford Univ., Dec. 2007 [PDF].