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Conference Paper: Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams

TitleDifferentially Private Continual Monitoring of Heavy Hitters from Distributed Streams
Authors
Issue Date2012
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 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?
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
Persistent Identifierhttp://hdl.handle.net/10722/160094
ISBN
ISSN
2014 SCImago Journal Rankings: 0.339

 

DC FieldValueLanguage
dc.contributor.authorChan, HTHen_US
dc.contributor.authorLi, Men_US
dc.contributor.authorShi, Een_US
dc.contributor.authorXu, Wen_US
dc.date.accessioned2012-08-16T06:03:09Z-
dc.date.available2012-08-16T06:03:09Z-
dc.date.issued2012en_US
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. 140-159en_US
dc.identifier.isbn9783642316791-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/160094-
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.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.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.titleDifferentially Private Continual Monitoring of Heavy Hitters from Distributed Streamsen_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, HTH: hubert@cs.hku.hken_US
dc.identifier.authorityChan, HTH=rp01312en_US
dc.identifier.doi10.1007/978-3-642-31680-7_8-
dc.identifier.scopuseid_2-s2.0-84864265207-
dc.identifier.hkuros202980en_US
dc.identifier.volume7384-
dc.identifier.spage140-
dc.identifier.epage159-
dc.publisher.placeGermany-

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