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Preventing history forgery with secure provenance

Preventing history forgery with secure provenance As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. While significant research has been conducted in this area, the associated security and privacy issues have not been explored, leaving provenance information vulnerable to illicit alteration as it passes through untrusted environments. In this article, we show how to provide strong integrity and confidentiality assurances for data provenance information at the kernel, file system, or application layer. We describe Sprov, our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes Sprov extremely easy to deploy. We present empirical results that show that, for real-life workloads, the runtime overhead of Sprov for recording provenance with confidentiality and integrity guarantees ranges from 1% to 13%, when all file modifications are recorded, and from 12% to 16%, when all file read and modifications are tracked. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Storage (TOS) Association for Computing Machinery

Preventing history forgery with secure provenance

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Access methods
ISSN
1553-3077
DOI
10.1145/1629080.1629082
Publisher site
See Article on Publisher Site

Abstract

As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. While significant research has been conducted in this area, the associated security and privacy issues have not been explored, leaving provenance information vulnerable to illicit alteration as it passes through untrusted environments. In this article, we show how to provide strong integrity and confidentiality assurances for data provenance information at the kernel, file system, or application layer. We describe Sprov, our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes Sprov extremely easy to deploy. We present empirical results that show that, for real-life workloads, the runtime overhead of Sprov for recording provenance with confidentiality and integrity guarantees ranges from 1% to 13%, when all file modifications are recorded, and from 12% to 16%, when all file read and modifications are tracked.

Journal

ACM Transactions on Storage (TOS) – Association for Computing Machinery

Published: Dec 1, 2009

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