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Conference Paper: Privacy-Preserving Aggregation of Time-Series Data

TitlePrivacy-Preserving Aggregation of Time-Series Data
Authors
Issue Date2011
PublisherInternet Society.
Citation
The 18th Annual Network & Distributed System Security Symposium (NDSS), San Diego, California, USA, 6-9 February 2011 How to Cite?
AbstractWe consider how an untrusted data aggregator can learn desired statistics over multiple participants’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised.
DescriptionSession 9: Privacy
The conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtml
Persistent Identifierhttp://hdl.handle.net/10722/135709

 

DC FieldValueLanguage
dc.contributor.authorShi, Een_US
dc.contributor.authorChan, HTHen_US
dc.contributor.authorRieffel, Een_US
dc.contributor.authorChow, Ren_US
dc.contributor.authorSong, Den_US
dc.date.accessioned2011-07-27T01:47:04Z-
dc.date.available2011-07-27T01:47:04Z-
dc.date.issued2011en_US
dc.identifier.citationThe 18th Annual Network & Distributed System Security Symposium (NDSS), San Diego, California, USA, 6-9 February 2011en_US
dc.identifier.urihttp://hdl.handle.net/10722/135709-
dc.descriptionSession 9: Privacy-
dc.descriptionThe conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtml-
dc.description.abstractWe consider how an untrusted data aggregator can learn desired statistics over multiple participants’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised.-
dc.languageengen_US
dc.publisherInternet Society.-
dc.relation.ispartofAnnual Network & Distributed System Security Symposium (NDSS)en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titlePrivacy-Preserving Aggregation of Time-Series Dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailChan, HTH: hubert@cs.hku.hken_US
dc.identifier.authorityChan, HTH=rp01312en_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros188720en_US
dc.publisher.placeUnited States-

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