{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:04:30Z","timestamp":1784145870443,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819235063","type":"print"},{"value":"9789819235070","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3507-0_8","type":"book-chapter","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:35:33Z","timestamp":1784144133000},"page":"92-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DeepVPT-Leak: Quantifying Privacy Risks in Parameter-Based Visual Prompting"],"prefix":"10.1007","author":[{"given":"Bosen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"8_CR1","first-page":"4582","volume-title":"Proc. ACL\/IJCNLP","author":"XL Li","year":"2021","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. In: Proc. ACL\/IJCNLP, pp. 4582\u20134597 (2021)"},{"key":"8_CR2","unstructured":"Wu, J., Li, X., Wei, C., et al.: Unleashing the power of visual prompting at the pixel level. Trans. Mach. Learn. Res. (2024)"},{"key":"8_CR3","first-page":"709","volume-title":"Proc. ECCV","author":"M Jia","year":"2022","unstructured":"Jia, M., Tang, L., Chen, B.-C., et al.: Visual prompt tuning. In: Proc. ECCV, pp. 709\u2013727. Springer (2022)"},{"key":"8_CR4","first-page":"3","volume-title":"Proc. IEEE S&P","author":"R Shokri","year":"2017","unstructured":"Shokri, R., Stronati, M., Song, C., et al.: Membership inference attacks against machine learning models. In: Proc. IEEE S&P, pp. 3\u201318. IEEE (2017)"},{"key":"8_CR5","volume-title":"Proc. NDSS. The Internet Society","author":"A Salem","year":"2019","unstructured":"Salem, A., Zhang, Y., Humbert, M., et al.: Ml-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: Proc. NDSS. The Internet Society (2019)"},{"key":"8_CR6","first-page":"5221","volume-title":"Proc. AISTATS","author":"Q Feng","year":"2025","unstructured":"Feng, Q., Kasa, S.R., Kasa, S.-K., et al.: Exposing privacy gaps: membership inference attack on preference data for llm alignment. In: Proc. AISTATS, pp. 5221\u20135229. PMLR (2025)"},{"key":"8_CR7","first-page":"343","volume-title":"Proc. ACM CCS","author":"D Chen","year":"2020","unstructured":"Chen, D., Yu, N., Zhang, Y., et al.: Gan-leaks: a taxonomy of membership inference attacks against generative models. In: Proc. ACM CCS, pp. 343\u2013362 (2020)"},{"key":"8_CR8","volume-title":"Proc. NDSS. The Internet Society","author":"Y Pang","year":"2025","unstructured":"Pang, Y., Wang, T.: Black-box membership inference attacks against fine-tuned diffusion models. In: Proc. NDSS. The Internet Society (2025)"},{"key":"8_CR9","first-page":"1605","volume-title":"Proc. USENIX Secur","author":"K Leino","year":"2020","unstructured":"Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated {white-box} membership inference. In: Proc. USENIX Secur, pp. 1605\u20131622 (2020)"},{"key":"8_CR10","first-page":"4920","volume-title":"Proc. CVPR","author":"Z Niu","year":"2016","unstructured":"Niu, Z., Zhou, M., Wang, L., et al.: Ordinal regression with multiple output CNN for age estimation. In: Proc. CVPR, pp. 4920\u20134928 (2016)"},{"key":"8_CR11","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical Report, University of Toronto (2009)"},{"key":"8_CR12","volume-title":"Proc. ICCV","author":"Z Liu","year":"2015","unstructured":"Liu, Z., Luo, P., Wang, X., et al.: Deep learning face attributes in the wild. In: Proc. ICCV (2015)"},{"key":"8_CR13","first-page":"5810","volume-title":"Proc. CVPR","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: Proc. CVPR, pp. 5810\u20135818 (2017)"},{"key":"8_CR14","first-page":"5841","volume-title":"Proc. USENIX Secur","author":"Y Wu","year":"2024","unstructured":"Wu, Y., Wen, R., Backes, M., et al.: Quantifying privacy risks of prompts in visual prompt learning. In: Proc. USENIX Secur, pp. 5841\u20135858 (2024)"},{"key":"8_CR15","unstructured":"Zhu, J., Lin, X., Qi, Y., et al.: GraphTheft: quantifying privacy risks in graph prompt learning. arXiv preprint arXiv:2411.14718 (2024)"},{"key":"8_CR16","first-page":"59","volume-title":"Proc. ACSAC Workshops","author":"T Nakai","year":"2024","unstructured":"Nakai, T., Oonishi, K., Higashi, T.: Does prompt-tuning enhance privacy in large language models? In: Proc. ACSAC Workshops, pp. 59\u201372. IEEE (2024)"},{"key":"8_CR17","first-page":"35313","volume-title":"Proc. AAAI","author":"Y Bian","year":"2026","unstructured":"Bian, Y., Zhang, X., et al.: Your prompts are not safe: output-free membership inference via prompt vectors in vision-language tuning. In: Proc. AAAI, pp. 35313\u201335321 (2026)"},{"key":"8_CR18","unstructured":"Li, W., Wang, L., Li, W., et al.: WebVision database: visual learning and understanding from web data. arXiv preprint arXiv:1708.02862 (2017)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3507-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:35:33Z","timestamp":1784144133000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3507-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"ISBN":["9789819235063","9789819235070"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3507-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"16 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}