I’m currently working as a researcher at Nokia Bell Labs in Antwerp, Belgium. Currently, my research focuses on applying privacy-enhancing technologies to practical problems.

Privacy-Enhancing Technologies (PETs)

We are currently investigating how to apply privacy-enhancing technologies (PETs) to practical problems in the telecom domain. Privacy-enhancing technologies are techniques that allow privacy to be preserved when dealing with data, e.g. by minimizing the amount of data that needs to be shared between multiple parties, preferably without losing functionality.

In particular, I am looking into Zero-Knowledge Proofs (ZKPs), a technique that allows one party to prove to another that a computation was performed correctly, without revealing the input data. This technique may be applied in cases where multiple parties want to exchange insights extracted from data, without revealing the raw data itself, e.g. because it is commercially sensitive or contains personal information.

One area that we are currently investigating is the use of ZKPs for telemetry data. In this context, Nokia supplies networking equipment to telecom operators. The operator may want to share insights extracted from telemetry data collected from the equipment, e.g. to resolve incidents or to help Nokia improve the equipment. However, the raw telemetry data may contain commercially sensitive information about the operator’s network, and thus cannot be shared directly. ZKPs allow the operator to share insights extracted from the telemetry data without revealing the raw data itself.

My interests lie especially in the design of programming languages, frameworks, or techniques that make it easier to develop real applications using privacy-enhancing technologies. Currently, developing with PETs is difficult, because the techniques are (mathematically) complex, tricky to use without accidentally opening up exploits, and limited in their functionality (e.g. only allowing computations on integers, or being very slow on longer computations). We aim to make it easy to use privacy-enhancing technologies, allowing them to be used to their full potential while ensuring that they are used correctly and efficiently.

World Wide Streams

Previously, I worked on World Wide Streams, a platform for real-time stream processing.

The emergence of the “Internet of Things” has lead to a proliferation of sensors and actuators that are constantly exchanging streams of data. These streams range from small sensor samples, such as reading a speedometer in a car, to high-bandwidth video streams, e.g. a dash-cam. Then, applications combine streams and continuously process their data to detect notable events and act upon them, e.g. detecting when a car might run into another and brake automatically.

Our team at Nokia Bell Labs developed World Wide Streams (WWS): a platform on which such applications for real-time stream processing can be developed. WWS can ingest and process data and video streams in many different formats. An application built on WWS consists of a dataflow, which is composed of several operators. These operators can be one of a set of built-in operators (ranging from map and filter to object detection) or custom-made by the developer. The operators are then “wired up” in a language called XStream, a library for TypeScript.