Hear from distinguished international Computer Science Professor on the subject of Big Data and Privacy.

Big Data & Privacy | International Guest Talk

Thursday, 20 July 2017, 03:45 pm

Location EF122
Contact annie.stazak@newcastle.edu.au

Professor Josep Domingo-Ferrer

Universitat Rovira i Virgili, Catalonia

Professor Josep Domingo-Ferrer, image by David Oliete Speaker's biography

Josep Domingo-Ferrer is a Distinguished Professor of Computer Science and an ICREA-Acadèmia Researcher at Universitat Rovira i Virgili, Tarragona, Catalonia, where he holds the UNESCO Chair in Data Privacy.

His research interests are in data privacy, data security, statistical disclosure control and cryptographic protocols, with a focus on the conciliation of privacy, security and functionality. He is a Fellow of IEEE and Distinguished Scientist of ACM. View more on his profile.


The explosion of big data opens such huge analytical and inferential possibilities that they may allow modeling the world and predicting its evolution with great accuracy. The dark side of such abundance of personal data is that it complicates the preservation of individual privacy.

Facing the tension between big data and privacy, we find two extreme positions that strive for hegemony: On the one side, the nihilists claim that it is delirious to try to maintain one's privacy in the big data world, and that the best we can hope for is that our data are not misused (if this means anything).

On the other hand, the fundamentalists propose privacy protection methods so drastic that their application would destroy nearly all the analytical interest of big data. We will survey these extreme positions and we will describe a midway path, which we believe more balanced and desirable.

This path is based on identifying the utility and privacy requirements of big data and trying to satisfy them through an evolution of the statistical disclosure control methods developed in the last 40 years.

In this respect, we will examine to what extent the two main families of privacy models, k-anonymity and differential privacy, are well-suited to anonymize big data.

We will also briefly touch on transparent, local and collaborative anonymization as ways to reduce the power of the data controller in front of individual subjects.