Privacy-Enhancing Technologies (PETs) help protect people’s data — even while it’s being used. In our group, we explore how to design practical cryptographic tools that let systems respect users’ privacy without giving up on functionality, accountability, or efficiency.

One big direction is Oblivious RAM (ORAM), which conceals not only the contents of data, but how it’s being accessed. That might sound abstract, but it’s crucial for things like secure cloud storage. We have contributed to making ORAM more efficient and practical, including the development of a tree-based construction [Read Here], which received a Test of Time award for its lasting impact.

We also work extensively on differential privacy, which allows organizations to identify patterns in data, such as public health trends or traffic statistics, without revealing any specific information about individuals. Our focus is on making these techniques practical in realistic, distributed settings where there is no central authority that must be trusted with raw data [Read Here].

Another area we focus on is anonymous credentials, systems that allow users to prove something about themselves (such as their age, nationality, or membership) without revealing their identity. This raises a broader question: how do we balance anonymity with accountability? We’ve developed systems where users remain anonymous unless they engage in misconduct. These ideas aren’t just theoretical, they’ve already been implemented in real-world platforms like Concordium, a Danish blockchain project that combines privacy with built-in regulatory compliance. 

We view Privacy-Enhancing Technologies as essential for building digital systems that respect both individual privacy and institutional trust. Our goal is not only to advance the theoretical foundations of PETs but also to develop tools that are practical, scalable, and ready for real-world adoption.