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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Data-model matching, typically achieved through direct contact, is critical to digital markets. However, when data and models belong to different owners, the direct contact-based form faces some security threats, including data security, privacy disclosure, and model reverse engineering attacks. A natural question emerges: Can effective data-model matching be achieved\n                    <jats:italic toggle=\"yes\">without direct contact<\/jats:italic>\n                    ? Previous methodologies can partially alleviate but not eliminate the necessity of direct contact between data and models, making security and privacy challenges persist throughout the matching process. In this article, our research findings indicate that, despite the essential differences between data and models, both can be represented using topological spaces. Therefore, we establish\n                    <jats:italic toggle=\"yes\">a unified metric<\/jats:italic>\n                    of data complexity and model expressivity from a topological perspective. The unified metric satisfies three conditions toward contactless data-model matching. Then, we develop a contactless matching paradigm, circumventing the necessity for direct contact between data and models and addressing privacy and security concerns. Specifically, we use topological data analysis to generate the data complexity topological descriptors (DCTDs) and use topological simplification to generate the model expressivity topological descriptors (METDs). We compute the matching degree and return the matching result. Through theoretical proof and experimental analysis, we validate the feasibility of the proposed contactless data-model matching paradigm in real-world scenarios.\n                  <\/jats:p>","DOI":"10.1145\/3774939","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T09:20:03Z","timestamp":1764235203000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Contactless Data-Model Matching"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4903-5111","authenticated-orcid":false,"given":"Zhiwei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai, China, and Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-0316","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai, China, Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai, China, and Shanghai Artificial Intelligence Laboratory, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/511446.511497"},{"issue":"8","key":"e_1_3_2_3_2","first-page":"1","article-title":"Persistence images: A stable vector representation of persistent homology","volume":"18","author":"Adams Henry","year":"2017","unstructured":"Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, and Lori Ziegelmeier. 2017. 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