PITA: Privacy through provenance abstraction

Daniel Deutch; Ariel Frankenthal; Amir Gilad; Yuval Moskovitch

doi:10.1109/icde51399.2021.00311

PITA: Privacy through provenance abstraction

Daniel Deutch
, Ariel Frankenthal
, Amir Gilad
, Yuval Moskovitch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
Publisher	IEEE Computer Society
Pages	2713-2716
Number of pages	4
ISBN (Electronic)	9781728191843
DOIs	https://doi.org/10.1109/icde51399.2021.00311
State	Published - Apr 2021
Externally published	Yes
Event	37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Online, Chania, Greece Duration: 19 Apr 2021 → 22 Apr 2021

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2021-April
ISSN (Print)	1084-4627
ISSN (Electronic)	2375-0286

Conference

Conference	37th IEEE International Conference on Data Engineering, ICDE 2021
Country/Territory	Greece
City	Virtual, Online, Chania
Period	19/04/21 → 22/04/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Explanations
K-anonymity
Privacy
Provenance

Access to Document

10.1109/icde51399.2021.00311

Cite this

Deutch, D., Frankenthal, A., Gilad, A., & Moskovitch, Y. (2021). PITA: Privacy through provenance abstraction. In Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021 (pp. 2713-2716). Article 9458839 (Proceedings - International Conference on Data Engineering; Vol. 2021-April). IEEE Computer Society. https://doi.org/10.1109/icde51399.2021.00311

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abstract = "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.",

keywords = "Explanations, K-anonymity, Privacy, Provenance",

author = "Daniel Deutch and Ariel Frankenthal and Amir Gilad and Yuval Moskovitch",

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year = "2021",

month = apr,

doi = "10.1109/icde51399.2021.00311",

language = "אנגלית",

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Deutch, D, Frankenthal, A, Gilad, A & Moskovitch, Y 2021, PITA: Privacy through provenance abstraction. in Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021., 9458839, Proceedings - International Conference on Data Engineering, vol. 2021-April, IEEE Computer Society, pp. 2713-2716, 37th IEEE International Conference on Data Engineering, ICDE 2021, Virtual, Online, Chania, Greece, 19/04/21. https://doi.org/10.1109/icde51399.2021.00311

PITA: Privacy through provenance abstraction. / Deutch, Daniel; Frankenthal, Ariel; Gilad, Amir et al.
Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021. IEEE Computer Society, 2021. p. 2713-2716 9458839 (Proceedings - International Conference on Data Engineering; Vol. 2021-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PITA: Privacy through provenance abstraction

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Keywords

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