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The authors consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, they wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Their protocols are scalable in settings where the volume of streaming data is large, since they guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.
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