PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites
Authors: Echo Meißner (Institute of Distributed Systems, Ulm University), Frank Kargl (Institute of Distributed Systems, Ulm University), Benjamin Erb (Institute of Distributed Systems, Ulm University), Felix Engelmann (MAX-IV Laboratory, Lund University)
Volume: 2025
Issue: 1
Pages: 632–653
DOI: https://doi.org/10.56553/popets-2025-0034
Abstract: Taking part in surveys, experiments, and studies is often compensated by rewards to increase the number of participants and encourage attendance. While privacy requirements are usually considered for participation, privacy aspects of the reward procedure are mostly ignored. To this end, we introduce PrePaMS, an efficient participation management system that supports prerequisite checks and participation rewards in a privacy-preserving way. Our system organizes participations with potential (dis-)qualifying dependencies and enables secure reward payoffs. By leveraging a set of proven cryptographic primitives and mechanisms such as anonymous credentials and zero-knowledge proofs, participations are protected so that service providers and organizers cannot derive the identity of participants even within the reward process. In this paper, we have designed and implemented a prototype of PrePaMS to show its effectiveness and evaluated its performance under realistic workloads. PrePaMS covers the information whether subjects have participated in surveys, experiments, or studies. When combined with other secure solutions for the actual data collection within these events, PrePaMS can represent a cornerstone for more privacy-preserving empirical research.
Keywords: practical privacy-enhancing systems, privacy-preserving systems, participation management, zero-knowledge proofs, anonymous credentials
Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution 4.0 license.
