The increasing overall passenger traffic by more vehicles and the development of autonomous vehicles will likely lead to increased popularity of ride-pooling platforms. While these platforms can counter the downsides of the traffic surge, they usually use centralized designs and collect vast amounts of user data. For example, the silent acquisition and storage of location data often go undetected by the average user but can reveal sensitive details about a person, potentially exposing them to privacy risks and unwanted surveillance. This thesis aims to visualize the privacy aspects of an opposed decentralized, privacy-preserving ride-pooling platform, GETACAR, proposed by Hüppelshäuser. In short, the platform connects two parties: Customers who request rides and Ride Providers who provide rides. It utilizes decentralized services, an Authentication Service, and a Matching Service that use a public blockchain to store every confirmed ride in a smart contract. The Authentication Services store all participants’ data locally, verify them, provide accountability, and prevent multiple identities. To further minimize the exposure of personal information, they create a new pseudonym for each interaction for each participant. Each party can use a local Matching Service with this pseudonym to find a suitable Customer and Ride Provider match utilizing cloaked location areas instead of precise geographic coordinates. If each party approves the match, a publicly visible smart contract is created on the blockchain using pseudonymized data to preserve the party’s identities from the public. Additionally, this contract allows each party to rate the other. Inspired by the Confidentiality Visualizer from KASTEL-MobilityLab, this visualization should not only explain the basics of the ride-pooling process but also highlight why the platform is privacy-preserving for Customers and Ride Providers and what information is exposed to the public and each actor of that platform.