Processing private queries over untrusted data cloud through privacy homomorphism

Haibo HU*, Jianliang XU, Chushi Ren, Koon Kau CHOI

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

142 Citations (Scopus)

Abstract

Query processing that preserves both the data privacy of the owner and the query privacy of the client is a new research problem. It shows increasing importance as cloud computing drives more businesses to outsource their data and querying services. However, most existing studies, including those on data outsourcing, address the data privacy and query privacy separately and cannot be applied to this problem. In this paper, we propose a holistic and efficient solution that comprises a secure traversal framework and an encryption scheme based on privacy homomorphism. The framework is scalable to large datasets by leveraging an index-based approach. Based on this framework, we devise secure protocols for processing typical queries such as k-nearest-neighbor queries (kNN) on R-tree index. Moreover, several optimization techniques are presented to improve the efficiency of the query processing protocols. Our solution is verified by both theoretical analysis and performance study.

Original languageEnglish
Title of host publication2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Pages601-612
Number of pages12
DOIs
Publication statusPublished - 2011
Event2011 IEEE 27th International Conference on Data Engineering, ICDE 2011 - Hannover, Germany
Duration: 11 Apr 201116 Apr 2011

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Conference

Conference2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Country/TerritoryGermany
CityHannover
Period11/04/1116/04/11

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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