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Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams

  • T. -H. Hubert Chan
  • Mingfei Li
  • Elaine Shi
  • Wenchang Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7384)

Abstract

We 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, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.

Keywords

Failure Probability Communication Cost Bloom Filter Current Window Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • T. -H. Hubert Chan
    • 1
  • Mingfei Li
    • 1
  • Elaine Shi
    • 2
  • Wenchang Xu
    • 3
  1. 1.The University of Hong KongHong Kong
  2. 2.UC BerkeleyUSA
  3. 3.Tsinghua UniversityChina

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