@inproceedings{c34811db8502411088f594f6a7b7474a,
title = "PITA: Privacy through provenance abstraction",
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",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 37th IEEE International Conference on Data Engineering, ICDE 2021 ; Conference date: 19-04-2021 Through 22-04-2021",
year = "2021",
month = apr,
day = "1",
doi = "10.1109/ICDE51399.2021.00311",
language = "English",
series = "Proceedings - International Conference on Data Engineering",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "2713--2716",
booktitle = "Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021",
address = "United States",
}