Shrinkwrap: Efficient SQL query processing in differentially private data federations

Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, Jennie Rogers

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a trusted data collector that can see all their data and their member databases cannot learn about individual records of other engines. Federations currently achieve this goal by evaluating queries obliviously using secure multiparty computation. This hides the intermediate result cardinality of each query operator by exhaustively padding it. With cascades of such operators, this padding accumulates to a blow-up in the output size of each operator and a proportional loss in query performance. Hence, existing private data federations do not scale well to complex SQL queries over large datasets. We introduce Shrinkwrap, a private data federation that offers data owners a differentially private view of the data held by others to improve their performance over oblivious query processing. Shrinkwrap uses computational differential privacy to minimize the padding of intermediate query results, achieving up to a 35X performance improvement over oblivious query processing. When the query needs differentially private output, Shrinkwrap provides a trade-off between result accuracy and query evaluation performance.

Original languageEnglish (US)
Pages (from-to)307-320
Number of pages14
JournalProceedings of the VLDB Endowment
Volume12
Issue number3
DOIs
StatePublished - Jan 1 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: Aug 26 2017Aug 30 2017

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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