PRIDA: PRIvacy-preserving data aggregation with multiple data customers

We propose PRIDA, a user-oriented private data aggregation solution involving multiple data customers. While most existing solutions focus on designing an efficiency-oriented data aggregation enabling input privacy only, we aim to provide more privacy for users and propose a data aggregation solution in which efficiency is kept in balance. We show that PRIDA provides a good performance level and is even better in timing evaluation than existing studies published recently (i.e., Bonawitz et al. (CCS’17), Corrigan-Gibbs et al. (NSDI’17), Bell et al. (CCS’20), Addanki et al. (SCN’22)). We employ threshold homomorphic encryption and secure two-party computation to ensure privacy properties. We balance the trade-off between a proper design for users and the desired privacy and efficiency.