PITA: Privacy through provenance abstraction

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Provenance is a valuable tool for explaining and validating query results. On the other hand, provenance also reveals much of the details about the query that generated it, which may include proprietary logic that the query owner does not wish to disclose. To this end, we propose to demonstrate PITA, a system designed to allow the release of provenance information, while hiding the properties of the underlying query. We formalize the trade-off between the level of information encoded in a provenance expression and the breach of privacy it incurs. Following this model, we design PITA to abstract the provenance so that it incurs minimum loss of information, while keeping privacy above a given threshold, namely protecting details of the original query from being revealed.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781728191843
StatePublished - 1 Apr 2021
Externally publishedYes
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021

Publication series

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


Conference37th IEEE International Conference on Data Engineering, ICDE 2021
CityVirtual, Chania


  • Explanations
  • K-anonymity
  • Privacy
  • Provenance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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