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1. Towards building a Fault Tolerant and Secure Open Science Chain

   https://doi.org/10.17605/OSF.IO/MJHK8  

   Shantharam, Manu; Sakai, Scott; Lin, Kai; Sivagnanam, Subhashini (October 2020, Gateways2020)

   null (Ed.)

   Scientific data, along with its analysis, accuracy, completeness, and reproducibility, plays a vital role in advancing science and engineering. Open Science Chain (OSC) provides a Cyberinfrastructure platform, built using distributed ledger technologies, where verification information about scientific dataset is stored and managed in a consortium blockchain. Researchers have the ability to independently verify the authenticity of scientific results using the information stored with OSC. Researchers can also build research workflows by linking data entries in the ledger and external repositories such as GitHub that will allow for detailed provenance tracking. OSC enables answers to questions such as: how can we ensure research integrity when different research groups share and work on the same datasets across the world? Is it possible to enable quick verification of the exact data sets that were used for particular published research? Can we check the provenance of the data used in the research? In this poster, we highlight our work in building a secure, scalable architecture for OSC including developing a security module for storing identities that can be used by the researchers of science gateways communities to increase the confidence of their scientific results. 

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2. Integrity Protection for Research Artifacts using Open Science Chain’s Command Line Utility

   https://doi.org/10.1145/3437359.3465587  

   Shantharam, Manu; Lin, Kai; Sakai, Scott; Sivagnanam, Subhashini (July 2021, PEARC '21: Practice and Experience in Advanced Research Computing)

   null (Ed.)

   Scientific data, its analysis, accuracy, completeness, and reproducibility play a vital role in advancing science and engineering. Open Science Chain (OSC) is a cyberinfrastructure platform built using the Hyperledger Fabric (HLF) blockchain technology to address issues related to data reproducibility and accountability in scientific research. OSC preserves the integrity of research datasets and enables different research groups to share datasets with the integrity information. Additionally, it enables quick verification of the exact datasets that were used for a particular published research and tracks its provenance. In this paper, we describe OSC’s command line utility that will preserve the integrity of research datasets from within the researchers’ environment or from remote systems such as HPC resources or campus clusters used for research. The Python-based command line utility can be seamlessly integrated within research workflows and provides an easy way to preserve the integrity of research data in OSC blockchain platform. 

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3. Introducing the Open Science Chain: Protecting Integrity and Provenance of Research Data

   https://doi.org/10.1145/3332186.3332203  

   Sivagnanam, Subhashini; Nandigam, Viswanath; Lin, Kai (January 2019, Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines)

   Data sharing is an integral component of research and academic publications, allowing for independent verification of results. Researchers have the ability to extend and build upon prior research when they are able to efficiently access, validate, and verify the data referenced in publications. Despite the well known benefits of making research data more open, data withholding rates have remained constant. Some disincentives to sharing research data include lack of credit, and fear of misrepresentation of data in the absence of context and provenance. While there are several research data sharing repositories that focus on making research data available, there are no cyberinfrastructure platforms that enable researchers to efficiently validate the authenticity of datasets, track the provenance, view the lineage of the data and verify ownership information. In this paper, we introduce and provide an overview of the NSF funded Open Science Chain, a cyberinfrastructure platform built using blockchain technologies that securely stores metadata and verification information about research data and tracks changes to that data in an auditable manner in order to address issues related to reproducibility and accountability in scientific research. 

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4. SciLedger: A Blockchain-based Scientific Workflow Provenance and Data Sharing Platform

   Reagan Hoopes; Hamilton Hardy; Min Long; Gaby G. Dagher (November 2022, IEEE International Conference on Collaboration and Internet Computing (CIC))

   Researchers collaborating from different locations need a method to capture and store scientific workflow provenance that guarantees provenance integrity and reproducibility. As modern science is moving towards greater data accessibility, researchers also need a platform for open access data sharing. We propose SciLedger, a blockchain-based platform that provides secure, trustworthy storage for scientific workflow provenance to reduce research fabrication and falsification. SciLedger utilizes a novel invalidation mechanism that only invalidates necessary provenance records. SciLedger also allows for workflows with complex structures to be stored on a single blockchain so that researchers can utilize existing data in their scientific workflows by branching from and merging existing workflows. Our experimental results show that SciLedger provides an able solution for maintaining academic integrity and research flexibility within scientific workflows. 

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5. Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

   https://doi.org/10.56553/popets-2022-0094  

   Halimi, Anisa; Dervishi, Leonard; Ayday, Erman; Pyrgelis, Apostolos; Troncoso-Pastoriza, Juan Ramón; Hubaux, Jean-Pierre; Jiang, Xiaoqian; Vaidya, Jaideep (July 2022, Proceedings on Privacy Enhancing Technologies)

   Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. In this work, we propose a framework that verifies the correctness of the aggregate statistics obtained as a result of a genome-wide association study (GWAS) conducted by a researcher while protecting individuals’ privacy in the researcher’s dataset. In GWAS, the goal of the researcher is to identify highly associated point mutations (variants) with a given phenotype. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another GWAS (conducted using publicly available datasets) to distinguish between correct statistics and incorrect ones. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results. 

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