Digital repositories, either digital preservation systems or archival systems, periodically check the integrity of stored objects to assure users of their correctness. To do so, prior solutions calculate integrity metadata and require the repository to store it alongside the actual data objects. This integrity metadata is essential for regularly verifying the correctness of the stored data objects. To safeguard and detect damage to this metadata, prior solutions rely on widely visible media, that is unaffiliated third parties, to store and provide back digests of the metadata to verify it is intact. However, they do not address recovery of the integrity metadata in case of damage or attack by an adversary. In essence, they do not preserve this metadata. We introduce IntegrityCatalog, a system that collects all integrity related metadata in a single component, and treats them as first class objects, managing both their integrity and their preservation. We introduce a treap-based persistent authenticated dictionary managing arbitrary length key value pairs, which we use to store all integrity metadata, accessible simply by object name. Additionally, IntegrityCatalog is a distributed system that includes a network protocol that manages both corruption detection and preservation of this metadata, using administrator-selected network peers with two possible roles. Verifiers store and offer attestations on digests and have minimal storage requirements, while preservers efficiently synchronize a complete copy of the catalog to assist in recovery in case of a detected catalog compromise on the local system. We describe our prototype implementation of IntegrityCatalog, measure its performance empirically, and demonstrate its effectiveness in real-world situations, with worst measured throughput of approximately 1K insertions per second, and 2K verified search operations per second.
Finally, Muniswamy- propose provenance information should be tracked by creating metadata objects and treating them as first class objects @cite_36 ; our system is complementary to theirs by taking over management of these metadata objects and assuring their full preservation. The same can be said for other provenance tracking systems, such as @cite_29 .
@cite_36 @cite_29
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The cloud is poised to become the next computing environment for both data storage and computation due to its pay-as-you-go and provision-as-you-go models. Cloud storage is already being used to back up desktop user data, host shared scientific data, store web application data, and to serve web pages. Today's cloud stores, however, are missing an important ingredient: provenance. Provenance is metadata that describes the history of an object. We make the case that provenance is crucial for data stored on the cloud and identify the properties of provenance that enable its utility. We then examine current cloud offerings and design and implement three protocols for maintaining data provenance in current cloud stores. The protocols represent different points in the design space and satisfy different subsets of the provenance properties. Our evaluation indicates that the overheads of all three protocols are comparable to each other and reasonable in absolute terms. Thus, one can select a protocol based upon the properties it provides without sacrificing performance. While it is feasible to provide provenance as a layer on top of today's cloud offerings, we conclude by presenting the case for incorporating provenance as a core cloud feature, discussing the issues in doing so. 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. In this paper, we show how to provide strong integrity and confidentiality assurances for data provenance information. We describe our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes it extremely easy to deploy. We present empirical results that show that, for typical real-life workloads, the run-time overhead of our approach to recording provenance with confidentiality and integrity guarantees ranges from 1 -13 .