This repository contains source code artifacts, experiments, and results associated with our PETS'25 paper. A publicly hosted test deployment can be accessed at https://prepams.uni-ulm.de, where anyone is able to explore our system using any device featuring a modern web browser.
More details about our constructions, prototype, and evaluation can be found in our PETS'25 publication:
Echo Meißner, Frank Kargl, Benjamin Erb, Felix Engelmann. 2025. PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites. In Proceedings on Privacy Enhancing Technologies (2025).
shared/- a rust-based WebAssembly module implementing the PrePaMS protocol.backend/- the PrePaMS server application that exposes an HTTP-based API for the PrePaMS web application.frontend/- the main client-side source code of the PrePaMS web application.evaluation/- evaluation artifacts to reproduce our performance evluation results (see Evaluation).results/- our evaluation results as reported in our paper.survey/- a very simple web-based survey system to showcase online studies.
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.
To assess the practicability of our proof-of-concept prototype, we have evaluated the performance of the PrePaMS primitives. The evaluation in this repository follows the Popper convention for reproducible evaluations.
The following results were obtained by running the evaluation on a desktop computer with an Intel Core i7-7700 (quad-core with SMT; 3.60 GHz) CPU and 32 GB RAM, running Ubuntu 22.04 LTS (GNU/Linux 5.4.0) with a headless Chrome 108. Additionally, we used a semi-automated build of the evaluation to evaluate the performance on different end-user devices, more precisely, an Android smartphone (Pixel 6 w/ Android 14 and Chrome 121) and an Apple tablet (3rd Gen iPad Pro 11” w/ iPadOS 17.2 and Safari 172).
Figure 1: Combined violin/jitter plots of measured execution times (in seconds) of our PrePaMS proof of concept implementation based on a synthetic workload with 𝑁 = 1000 participants, 𝑀 = 1000 participations, and 𝑂 = 100 payouts. The individual protocols are segmented by the role of the executing party (P: Participant, O: Organizer, S Service) and partially replicated across different device types (i.e.,
desktop,
tablet, and
smartphone).
Figure 2: Plot of measured median execution times in seconds of the participation protocol based on a synthetic workload with either qualifier (×, 𝑞), disqualifier (+, 𝑑), range constraints (⋄, 𝑟), or set constraints (▽, 𝑠) varied from 𝑛 ∈ [0..10] and all other parameters pinned to 0.
The Popper workflow in this repository can be used to replicate results, compute statistics, and generate plots from the evaluation results.
Assuming both Docker and the Popper CLI tool are installed, you can simply call popper run in the root directory of the repository to run the workflow.
In case the Popper CLI tool is not installed, you can alternatively use the provided ./popper.sh helper script, which executes the Popper CLI in a docker container.
The evaluation artifacts have only been tested on 64-bit Linux systems, other operating systems or architectures may require additional steps.
Our Popper workflow (see .popper.yml) consists of two steps:
measure- Uses puppeteer to log the time it takes to execute the synthetic workload of PrePaMS operations.analyze- Computes basic statistics on the time data collected in the previous step and generates a box plot.
Post execution the evaluation/results/ directory will contain the following files:
performance/register.csv- Measurements of the register protocol.performance/participations.csv- Measurements of the participation protocol.performance/payout.csv- Measurements of the payout protocol.performance.pdf- A violin plot showing the processing times of the measured algorithms.{qualifier,disqualifier,set-constraints,range-constraints}-%d/participations.csv- Measurements of the participation protocol with varying prerequisites.scaling.pdf- A min/max linechart showing the processing time of the participation protocol in relation to the number of qualifier,disqualifier, and attribute constraints.
The evaluation/ directory also contains means to measure other devices, such as smartphones or tablets.
This step is skipped by default during evaluation using popper.
To enable it remove the SKIP_STEP: "REMOVE THIS LINE TO ENABLE THE EVALUATION SERVER" line from the .popper.yml file.
Alternatively, executing npm run build evaluation/ in the evaluation directory compiles a static version of the performance evaluation and outputs it to the evaluation/dist directory.
Then evaluation/serve.js can be used to run a simple web server which hosts this static evaluation app, as well as an HTTP-based API to retrieve measurement results from other devices.
A ready to use version is deployed under https://prepams.uni-ulm.de/eval/, ready to evaluate the performance of PrePaMS on any device featuring a modern browser.
popper run analyze can be used to update the resulting plot scaling.pdf with the additional devices.
The PrePaMS prototype and related artifacts are licensed under the terms of the AGPL-3.0 license, unless otherwise noted.