Beyond Statistical Estimation: Differentially Private Individual Computation via ShufflingThis repository contains implementations of various privacy-preserving algorithms in the PIC model (tested with Python 3.10). The projects included are:1. Spatial Crowdsourcing in the PIC Model, refer to `SpatialCrowdsourcing`.2. Location-based Nearest Neighbors in the PIC Model, refer to `LocationBasedSocialSystem`.3. Federated Learning with Incentives in the PIC Model, refer to `FederatedLearningIncentives`## SetupTo get started with the projects, you need to go to sub-directory and install the application-specific dependencies. You can do this by running the following command:```bashpip install -r requirements.txt``` Notice: This repository includes code and datasets from third parties for experimental performance comparisons. Please adhere to their respective licenses accordingly. The CC BY-NC-AD 4.0 International license applies to all other proprietary artifacts created by us.