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Provenance Segmentation

Authors: 

Rui Abreu, Palo Alto Research Center; Dave Archer and Erin Chapman, Galois, Inc.; James Cheney, University of Edinburgh; Hoda Eldardiry, Palo Alto Research Center; Adrià Gascón, University of Edinburgh

Abstract: 

Using pervasive provenance to secure mainstream systems has recently attracted interest from industry and government. Recording, storing and managing all of the provenance associated with a system is a considerable challenge. Analyzing the resulting noisy, heterogeneous, continuously-growing provenance graph adds to this challenge, and apparently necessitates segmentation, that is, approximating, compressing or summarizing part or all of the graph in order to identify patterns or features. In this paper, we describe this new problem space for provenance data management, contrast it with related problem spaces addressed by prior work on provenance abstraction and sanitization, and highlight challenges and future directions toward solutions to the provenance segmentation problem.

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BibTeX
@inproceedings {196984,
title = {Provenance Segmentation},
booktitle = {8th USENIX Workshop on the Theory and Practice of Provenance (TaPP 16)},
year = {2016},
address = {Washington, D.C.},
url = {https://www.usenix.org/conference/tapp16/workshop-program/presentation/abreu},
publisher = {USENIX Association},
month = jun,
}
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