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Conference Paper: Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams
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TitleDifferentially Private Continual Monitoring of Heavy Hitters from Distributed Streams
 
AuthorsChan, HTH
Li, M1
Shi, E2
Xu, W3
 
Issue Date2012
 
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
 
CitationThe 12th International Symposium on Privacy Enhancing Technologies (PETS), Vigo, Spain, 11-13 July 2012. In Lecture Notes in Computer Science, 2012, v. 7384, p. 140-159 [How to Cite?]
DOI: http://dx.doi.org/10.1007/978-3-642-31680-7_8
 
AbstractWe 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.
 
DescriptionLecture Notes in Computer Science, vol. 7384 entitled: Privacy enhancing technologies: 12th international symposium, PETS 2012, Vigo, Spain, July 11-13, 2012: proceedings
 
ISBN9783642316791
 
ISSN0302-9743
2013 SCImago Journal Rankings: 0.310
 
DOIhttp://dx.doi.org/10.1007/978-3-642-31680-7_8
 
DC FieldValue
dc.contributor.authorChan, HTH
 
dc.contributor.authorLi, M
 
dc.contributor.authorShi, E
 
dc.contributor.authorXu, W
 
dc.date.accessioned2012-08-16T06:03:09Z
 
dc.date.available2012-08-16T06:03:09Z
 
dc.date.issued2012
 
dc.description.abstractWe 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.
 
dc.descriptionLecture Notes in Computer Science, vol. 7384 entitled: Privacy enhancing technologies: 12th international symposium, PETS 2012, Vigo, Spain, July 11-13, 2012: proceedings
 
dc.identifier.citationThe 12th International Symposium on Privacy Enhancing Technologies (PETS), Vigo, Spain, 11-13 July 2012. In Lecture Notes in Computer Science, 2012, v. 7384, p. 140-159 [How to Cite?]
DOI: http://dx.doi.org/10.1007/978-3-642-31680-7_8
 
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-642-31680-7_8
 
dc.identifier.epage159
 
dc.identifier.hkuros202980
 
dc.identifier.isbn9783642316791
 
dc.identifier.issn0302-9743
2013 SCImago Journal Rankings: 0.310
 
dc.identifier.scopuseid_2-s2.0-84864265207
 
dc.identifier.spage140
 
dc.identifier.urihttp://hdl.handle.net/10722/160094
 
dc.identifier.volume7384
 
dc.languageeng
 
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
 
dc.publisher.placeGermany
 
dc.relation.ispartofLecture Notes in Computer Science
 
dc.rightsThe original publication is available at www.springerlink.com
 
dc.titleDifferentially Private Continual Monitoring of Heavy Hitters from Distributed Streams
 
dc.typeConference_Paper
 
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Author Affiliations
  1. The University of Hong Kong
  2. UC Berkeley
  3. Tsinghua University