Data, privacy & the individual

Data is the main fuel of our digital economies. Our financial transactions, movements, communications, relationships and interactions with governments and businesses, both online and off, generate data that is collected, bought and sold by data brokers and corporations interested in profiling individuals.

As the collection and analysis of data becomes more sophisticated and accurate, and as data sets grow to become Big Data, the opportunities ahead seem infinite. The risks, however, are also great, as the information being handled about individuals is extremely sensitive. This research project explores some of the key ethical questions posed by today’s emerging technologies, and analyzes new technical methods that governments and companies can use to profit from information while respecting regulations and maintaining the trust of both their clients and citizens.

The Project

Our research project will explore these issues by conducting applied, multidisciplinary research at two different levels:

Macro level: Privacy, Ethics and the Individual

Seating in her office, a data scientist can have access to more information about any individual in the world than any intelligence agency in pre-Internet times. With methods of aggregation, data scientists can determine people’s fears, desires, weaknesses, habits, relationships, and more. This presents overwhelming new opportunities for both the public and the private sectors, but also major ethical dilemmas and social threats. This line of research will address some of them by researching:

  • what is privacy, why is it important, what are the risks of breaching people’s privacy, and what are the requirements to respect it.
  • what kinds of information is it ethical for institutions to collect and/or infer
  • what is the moral significance of Big Data (as opposed to data).

Micro level: Differential Privacy

Once having collected data, institutions face the challenge of determining how to use it without infringing the right to privacy of their clients. One of the most promising ways to do so is through differential privacy—a cryptographic framework designed to introduce enough mathematical noise into a database such that researchers can still derive useful and accurate conclusions from the data without having the power to access or identify individual records. This line of research will:

  • analyze the strengths and weaknesses of this new method of processing data.
  • study cases of success and failure in international companies and research settings.
  • identify what kinds of information and uses are more appropriate to differential privacy processes.
  • explore how institutions can comply with the latest EU regulations on privacy and data usage by implementing this method.
  • determine how should the EU regulatory frameworks change to meet the customers’ rights and the companies’ need to compete in the global market.


Carissa Véliz

University of Oxford

Julia Powles

University of Cambridge

Yves-Alexandre de Montjoye

Imperial College London

Ricard Martínez

Universitat de Valencia

Joss Wright

University of Oxford

Kevin Macnish

University of Twente

Jess Whittlestone

University of Cambridge

Paul Francis

Max Planck Institute

Verena Risse

TU Dortmund

Alfred Archer

Tilburg University

Karina Vold

University of Cambridge

Jordi Soria-Comas

Catalan Data Protection Authority

Nathan Wildman

Tilburg University

In partnership with: