CamFlow is a modular Linux implementation of data provenance capture. CamFlow stands for Cambridge Flow Architecture, the Cam is also the river that flows through Cambridge, UK.

View CamFlow github project

Practical Linux Provenance

CamFlow is a Linux Security Modules (LSMs) designed to capture data provenance for the purpose of system audit (version prior to v0.2.0 also support Decentralised Information Flow Control enforcement). CamFlow can stack with existing security modules such as SELinux.

Installing CamFlow

For instructions on how to install CamFlow visit this link. We also provide vagrant scripts to easily setup virtual machines running CamFlow. The source code is available on our github repository. Please do not hesitate to fork our project or create issues to report bugs.


The easiest way to contribute to CamFlow is by submitting issues to suggest improvement or report bug. When reporting a bug, please specify the version of CamFlow you are running and your Linux distribution. To contribute new feature, please fork the repository of the component you wish to improve, and submit a pull request against the dev branch. The pull request must pass the continuous integration test before it can be merged.

Research Project

CamFlow is the result of research at the University of Cambridge, Computer Laboratory, Opera Research Group. The project was funded by the Engineering and Physical Sciences Research Council (EPSRC, UK) under the CloudSafetyNet research grant.

From July 2016, the development is being supported at Harvard University's Center for Research on Computation and Society as part of the End to End Provenance (eeProv) project.


CamFlow is discussed in the following publications. Details given in these papers may be outdated - please refer to the code if in doubt, or contact us.

Pasquier T., Han X., Goldstein M., Moyer T., Eyers D., Seltzer M. and J. Bacon Practical Whole-System Provenance Capture. Symposium on Cloud Computing (SoCC'17) (2017), ACM. .pdf bib

Han X., Pasquier T., Ranjan T., Goldstein M., and Seltzer M. FRAPpuccino: Fault-detection through Runtime Analysis of Provenance. Workshop on Hot Topics in Cloud Computing (HotCloud'17) (2017), USENIX. .pdf bib website

Pasquier T. and Eyers D. Information Flow Audit for Transparency and Compliance in the Handling of Personal Data. IC2E International Workshop on Legal and Technical Issues in Cloud Computing (CLaw'16) (2016), IEEE. .pdf bib 10.1109/IC2EW.2016.29

Pasquier T., Singh J., , Bacon J., and Eyers D. Information Flow Audit for PaaS Clouds. In International Conference on Cloud Engineering (IC2E) (2016), IEEE. .pdf bib 10.1109/IC2E.2016.19

Pasquier T., Singh J., Eyers D., and Bacon J. CamFlow: Managed Data-Sharing for Cloud Services. IEEE Transactions on Cloud Computing (2015), IEEE. .pdf .bib 10.1109/TCC.2015.2489211