Yuval Moskovitch

Senior Academic

PITA

Privacy through provenance abstraction

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch

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.

Publication language English
Pages 2713-2716
Publication status Published - 01.04.2021
Article Number 9458839

Keywords

Explanations
K-anonymity
Privacy
Provenance

ASJC Scopus subject areas

Software
Signal Processing
Information Systems