Rights The original publication is available at www.springerlink.com Privacy-Preserving Stream Aggregation with Fault Tolerance

Abstract. We consider applications where an untrusted aggregator would like to collect privacy sensitive data from users, and compute aggregate statistics pe-riodically. For example, imagine a smart grid operator who wishes to aggregate the total power consumption of a neighborhood every ten minutes; or a market researcher who wishes to track the fraction of population watching ESPN on an hourly basis. We design novel mechanisms that allow an aggregator to accurately estimate such statistics, while offering provable guarantees of user privacy against the untrusted aggregator. Our constructions are resilient to user failure and compromise, and can efficiently support dynamic joins and leaves. Our constructions also exem-plify the clear advantage of combining applied cryptography and differential pri-vacy techniques.