GhostDB: Querying visible and hidden data without leaks

Nicolas Anciaux, Mehdi Benzine, Luc Bouganim, Philippe Pucheral, Dennis Shasha

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

Abstract

Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB key (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB key). This paper presents the basic framework to make this all work intuitively and efficiently.

Original languageEnglish (US)
Title of host publicationSIGMOD 2007
Subtitle of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages677-688
Number of pages12
DOIs
StatePublished - 2007
EventSIGMOD 2007: ACM SIGMOD International Conference on Management of Data - Beijing, China
Duration: Jun 12 2007Jun 14 2007

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

OtherSIGMOD 2007: ACM SIGMOD International Conference on Management of Data
CountryChina
CityBeijing
Period6/12/076/14/07

Keywords

  • Privacy
  • Secure device
  • Storage model

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Anciaux, N., Benzine, M., Bouganim, L., Pucheral, P., & Shasha, D. (2007). GhostDB: Querying visible and hidden data without leaks. In SIGMOD 2007: Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 677-688). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1247480.1247555