Distributed query processing is an effective means for processing large amounts of data. To abstract from the technicalities of distributed systems, algorithms for operator placement automatically distribute sequential data queries over the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data. We propose a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. Our suggested algorithm has two phases. First we generate deployment candidates based on privacy constraints, based on an information-flow type system to reason about the sensitivity of query computations. Second, the best placement among the candidates is selected by a cost model that maximizes performance.