Distributed incremental processing is an effective solution for processing large amounts of data in an efficient way. In this setting, algorithms for operator placement automatically distribute data queries the the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data.

We present ongoing research on a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. We implement a working prototype based on previous work on (local) incremental computation.