{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T07:03:21Z","timestamp":1763795001225,"version":"3.45.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"12","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U22B2029"],"award-info":[{"award-number":["U22B2029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Laboratory of Intelligent Space TTC&O (Space Engineering University), Ministry of Education","award":["CYK2024-02-02"],"award-info":[{"award-number":["CYK2024-02-02"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n                    Non-fungible token (NFT) markets present a dual analytical challenge: integrating heterogeneous data modalities (high-dimensional visual features and discrete transaction sequences) while preserving privacy for sensitive wallet addresses and trading strategies. Current approaches analyze visual attributes or transaction patterns in isolation, missing critical value drivers from cross-modal interactions. Meanwhile, existing multimodal techniques lack formal privacy guarantees, exposing participants to inference attacks. This article introduces PrivaMod, a privacy-preserving Bayesian framework that addresses these limitations through uncertainty-aware multimodal fusion. Our approach implements precision-weighted Bayesian fusion that dynamically adjusts modality contributions based on quantified uncertainty levels, while integrating R\u00e9nyi Differential Privacy throughout the pipeline via calibrated noise injection and adaptive gradient clipping. Evaluated on 167,492 CryptoPunk transactions, PrivaMod achieves a market efficiency score of 0.874 and R\n                    <jats:sup>2<\/jats:sup>\n                    of 0.912, outperforming existing methods by 13.4% through superior cross-modal integration while maintaining strong privacy guarantees (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\varepsilon\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    \u00a0=\u00a00.08,\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\delta\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    \u00a0=\u00a01e-5) with membership inference attack success rates near random guessing (53.4%). The system demonstrates that privacy-preserving techniques can enhance rather than compromise analytical performance, establishing a foundation for responsible market analysis.\n                  <\/jats:p>\n                  <jats:p>\n                    To ensure reproducibility, we release our code, preprocessed datasets, and model checkpoints with detailed documentation and scripts to replicate all experiments. PrivaMod is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/kvjunior\/PrivaMod\/blob\/main\/README.md\">https:\/\/github.com\/kvjunior\/PrivaMod\/blob\/main\/README.md<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3762999","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T15:01:35Z","timestamp":1756134095000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PrivaMod: Uncertainty-Aware Multimedia Fusion with Privacy Guarantees for NFT Visual and Transaction Analysis"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0259-8135","authenticated-orcid":false,"given":"Victor","family":"Kombou","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2245-2588","authenticated-orcid":false,"given":"Qi","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9851-666X","authenticated-orcid":false,"given":"Hu","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-6417","authenticated-orcid":false,"given":"Jianbin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-849X","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ministry of Education, Key Laboratory of Intelligent Space TTC&amp;O (Space Engineering University), Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9381-941X","authenticated-orcid":false,"given":"Benjamin Fabien","family":"Eyezo\u2019o","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2316-0664","authenticated-orcid":false,"given":"Stephane Richard","family":"Befoum","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1905-4509","authenticated-orcid":false,"given":"Jonathan","family":"Anto Leoba","sequence":"additional","affiliation":[{"name":"School of Nuclear and Automation Engineering, Chengdu University of Technology, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9533-2458","authenticated-orcid":false,"given":"Brinda Leaticia","family":"Kuiche Sop","sequence":"additional","affiliation":[{"name":"College of Management Science, Chengdu University of Technology, Chengdu, 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