Blender: Enabling local search with a hybrid differential privacy model

Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, Benjamin Livshits

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

Abstract

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the most popular records of a web search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets comprising 4.8 million and 13.2 million unique queries respectively, our approach attains NDCG values exceeding 95% across a range of commonly used privacy budget values.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th USENIX Security Symposium
PublisherUSENIX Association
Pages747-764
Number of pages18
ISBN (Electronic)9781931971409
StatePublished - 2017
Externally publishedYes
Event26th USENIX Security Symposium - Vancouver, Canada
Duration: Aug 16 2017Aug 18 2017

Publication series

NameProceedings of the 26th USENIX Security Symposium

Conference

Conference26th USENIX Security Symposium
Country/TerritoryCanada
CityVancouver
Period8/16/178/18/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Safety, Risk, Reliability and Quality

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