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
1575826986 1858703999 195157153 2115466528
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 . Digital provenance describes the ancestry or history of a digital object. Most existing provenance systems, however, operate at only one level of abstraction: the system call layer, a workflow specification, or the high-level constructs of a particular application. The provenance collectable in each of these layers is different, and all of it can be important. Single-layer systems fail to account for the different levels of abstraction at which users need to reason about their data and processes. These systems cannot integrate data provenance across layers and cannot answer questions that require an integrated view of the provenance. We have designed a provenance collection structure facilitating the integration of provenance across multiple levels of abstraction, including a workflow engine, a web browser, and an initial runtime Python provenance tracking wrapper. We layer these components atop provenance-aware network storage (NFS) that builds upon a Provenance-Aware Storage System (PASS). We discuss the challenges of building systems that integrate provenance across multiple layers of abstraction, present how we augmented systems in each layer to integrate provenance, and present use cases that demonstrate how provenance spanning multiple layers provides functionality not available in existing systems. Our evaluation shows that the overheads imposed by layering provenance systems are reasonable. Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources. The provenance of data products generated by complex transformations such as workflows is of considerable value to scientists. From it, one can ascertain the quality of the data based on its ancestral data and derivations, track back sources of errors, allow automated re-enactment of derivations to update a data, and provide attribution of data sources. Provenance is also essential to the business domain where it can be used to drill down to the source of data in a data warehouse, track the creation of intellectual property, and provide an audit trail for regulatory purposes. In this paper we create a taxonomy of data provenance techniques, and apply the classification to current research efforts in the field. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. Our synthesis can help those building scientific and business metadata-management systems to understand existing provenance system designs. The survey culminates with an identification of open research problems in the field. Digital provenance is meta-data that describes the ancestry or history of a digital object. Most work on provenance focuses on how provenance increases the value of data to consumers. However, provenance is also valuable to storage providers. For example, provenance can provide hints on access patterns, detect anomalous behavior, and provide enhanced user search capabilities. As the next generation storage providers, cloud vendors are in the unique position to capitalize on this opportunity to incorporate provenance as a fundamental storage system primitive. To date, cloud offerings have not yet done so. We provide motivation for providers to treat provenance as first class data in the cloud and based on our experience with provenance in a local storage system, suggest a set of requirements that make provenance feasible and attractive.