Observed vs. Possible Provenance (Research Track)


Tom Blount and Adriane Chapman, University of Southampton; Michael Johnson, Max Planck Institute for Radio Astronomy; Bertram Ludascher, University of Illinois Urbana-Champaign


Provenance has been of interest to the Computer Science community for nearly two decades, with proposed uses ranging from data authentication, to security auditing, to ensuring trust in decision making processes. However, despite its enthusiastic uptake in the academic community, its adoption elsewhere is often hindered by the cost of implementation. In this paper we seek to alleviate some of these factors, and propose the idea of possible provenance in which we relax the constraint that provenance must be directly observed. We categorise some existing approaches to gathering provenance and compare the costs and benefits of each, and illustrate one method for generating possible provenance in more detail with a simple example: inferring the possible provenance of a game of Connect Four. We then go on to discuss some of the benefits and ramifications of this approach to gathering provenance, and suggest some key next steps in advancing this research.

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@inproceedings {274849,
author = {Tom Blount and Adriane Chapman and Michael Johnson and Bertram Lud{\"a}scher},
title = {Observed vs. Possible Provenance (Research Track)},
booktitle = {13th International Workshop on Theory and Practice of Provenance (TaPP 2021)},
year = {2021},
url = {https://www.usenix.org/conference/tapp2021/presentation/blount},
publisher = {USENIX Association},
month = jul