{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260100789"},"data":{"type":"grant-details","id":"DE260100789","attributes":{"code":"DE260100789","administering-organisation":"Victoria University","announcement-administering-organisation":"Victoria University","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Quality-Aware and Verifiable Data Valuation in Decentralized Networks. Unlocking fragmented data resources to enhance the fairness and trustworthiness of collaborations in decentralized networks is a vital challenge. This project aims to develop a quality-aware and verifiable data valuation framework for the challenge. The framework generates new knowledge in decentralized data ecosystems through innovative approaches to data representation, multi-dimensional quality assessment, and secure verifications. Project outcomes include theoretical advancements in data valuation and a prototype system demonstrating cross-organizational data collaboration. It strengthens national security and improves healthcare delivery by filtering malicious data and utilizing valuable data to build robust systems across the nation.","funding-current":534117.00,"funding-at-announcement":529878,"investigators-current":[{"title":"Dr","firstName":"Jie","familyName":"Xu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-9924-4157 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Jie","familyName":"Xu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-9924-4157 "}],"organisations-current":[{"organisationName":"Victoria University","roleName":"Administering Organisation","state":"VIC"}],"organisations-at-announcement":[{"organisationName":"Victoria University","roleName":"Administering Organisation","state":"VIC"}],"field-of-research":[{"isPrimary":false,"code":"460403","name":"Data Security and Protection","type":"FOR20"},{"isPrimary":true,"code":"4605","name":"Data Management and Data Science","type":"FOR20"},{"isPrimary":false,"code":"460502","name":"Data Mining and Knowledge Discovery","type":"FOR20"},{"isPrimary":false,"code":"460605","name":"Distributed Systems and Algorithms","type":"FOR20"}],"socio-economic-objective":[{"code":"220403","name":"Artificial Intelligence","type":"SEO20"},{"code":"220408","name":"Information Systems","type":"SEO20"}],"international-collaboration":["Canada","China (excludes SARs and Taiwan)","Hong Kong (SAR of China)","Switzerland","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project aims to develop a quality-aware and verifiable data valuation framework in decentralized networks, addressing fragmented data challenges that limit cross-organizational machine learning. The project addresses three research gaps: inefficient data discovery across organizations, biased data valuation methods, and lack of value verification that undermines trust between participants. By designing comprehensive quality-aware data valuation with verification guarantees, this project enhances the fairness and trustworthiness in machine learning collaboration.\nThere are three key benefits for Australia. Firstly, it will support healthy and thriving communities by enabling more reliable machine learning applications in healthcare and public services. Secondly, it will create new economic opportunities through fair and efficient data marketplaces, where Australian organizations can monetize their high-quality data assets and access verified quality data. Thirdly, it will contribute to building a secure and resilient nation by protecting critical systems from low-quality or malicious data.\nTo maximize impact, the project will release open-source tools and share findings through public engagement and education programs. The project outcomes could be incorporated into university curricula to train future practitioners and provide long-term benefits for the Australian machine learning ecosystem."}}}