{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:33:36Z","timestamp":1755225216698,"version":"3.43.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,25]]},"DOI":"10.1145\/3708821.3733915","type":"proceedings-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T06:33:18Z","timestamp":1755066798000},"page":"488-500","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1328-3773","authenticated-orcid":false,"given":"Ruining","family":"Sun","sequence":"first","affiliation":[{"name":"Xiangtan University, Xiangtan, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4455-4227","authenticated-orcid":false,"given":"Hongsheng","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Newcastle, Newcastle, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4711-7543","authenticated-orcid":false,"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"Deakin University, Burwood, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3813-2776","authenticated-orcid":false,"given":"Zhaoxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5611-3483","authenticated-orcid":false,"given":"Yanjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2495-0305","authenticated-orcid":false,"given":"Haizhuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Xiangtan University, Xiangtan, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9330-2662","authenticated-orcid":false,"given":"Leo Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Griffith University, Southport, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,24]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Hangbo Bao Li Dong Songhao Piao and Furu Wei. 2021. BEiT: BERT Pre-Training of Image Transformers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2106.08254 (2021)."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"e_1_3_3_2_4_2","unstructured":"Mathilde Caron Ishan Misra Mairal et\u00a0al. 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33 (2020) 9912\u20139924."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179334"},{"key":"e_1_3_3_2_6_2","first-page":"1597","volume-title":"International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. PMLR, 1597\u20131607."},{"key":"e_1_3_3_2_7_2","unstructured":"Xinlei Chen Haoqi Fan Ross Girshick and Kaiming He. 2020. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2003.04297 (2020)."},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"e_1_3_3_2_10_2","unstructured":"Emiliano De\u00a0Cristofaro. 2020. An overview of privacy in machine learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2005.08679 (2020)."},{"key":"e_1_3_3_2_11_2","unstructured":"Alexey Dosovitskiy. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.11929 (2020)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26731"},{"key":"e_1_3_3_2_13_2","unstructured":"Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.07728 (2018)."},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00649"},{"key":"e_1_3_3_2_15_2","unstructured":"Jean-Bastien Grill Florian Strub Altch\u00e9 et\u00a0al. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems 33 (2020) 21271\u201321284."},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Jie Gui Tuo Chen Jing Zhang Qiong Cao Zhenan Sun Hao Luo and Dacheng Tao. 2024. A Survey on Self-supervised Learning: Algorithms Applications and Future Trends. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"e_1_3_3_2_17_2","first-page":"228","volume-title":"Medical Imaging with Deep Learning","author":"Gupta Umang","year":"2021","unstructured":"Umang Gupta, Dimitris Stripelis, Pradeep\u00a0K Lam, Paul Thompson, Jose\u00a0Luis Ambite, and Greg Ver\u00a0Steeg. 2021. Membership inference attacks on deep regression models for neuroimaging. In Medical Imaging with Deep Learning. PMLR, 228\u2013251."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"e_1_3_3_2_19_2","unstructured":"Jamie Hayes Luca Melis George Danezis and Emiliano De\u00a0Cristofaro. 2017. Logan: Membership inference attacks against generative models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1705.07663 (2017)."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Hongsheng Hu Zoran Salcic Lichao Sun Gillian Dobbie Philip\u00a0S Yu and Xuyun Zhang. 2022. Membership inference attacks on machine learning: A survey. ACM Computing Surveys (CSUR) 54 11s (2022) 1\u201337.","DOI":"10.1145\/3523273"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Malhar\u00a0S Jere Tyler Farnan and Farinaz Koushanfar. 2020. A taxonomy of attacks on federated learning. IEEE Security & Privacy 19 2 (2020) 20\u201328.","DOI":"10.1109\/MSEC.2020.3039941"},{"key":"e_1_3_3_2_25_2","unstructured":"Alex Krizhevsky Geoffrey Hinton et\u00a0al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.96"},{"key":"e_1_3_3_2_27_2","unstructured":"Ya Le and Xuan\u00a0S. Yang. 2015. Tiny ImageNet Visual Recognition Challenge. https:\/\/api.semanticscholar.org\/CorpusID:16664790"},{"key":"e_1_3_3_2_28_2","unstructured":"Shuhao Li Yajie Wang Yuanzhang Li and Yu-an Tan. 2022. l-Leaks: Membership inference attacks with logits. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2205.06469 (2022)."},{"key":"e_1_3_3_2_29_2","unstructured":"Zheng Li Xinlei He Ning Yu and Yang Zhang. 2024. Membership Inference Attack Against Masked Image Modeling. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2408.06825 (2024)."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484575"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484749"},{"key":"e_1_3_3_2_32_2","first-page":"4525","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Liu Yugeng","year":"2022","unstructured":"Yugeng Liu, Rui Wen, He Xinlei, et\u00a0al. 2022. ML-Doctor: Holistic risk assessment of inference attacks against machine learning models. In 31st USENIX Security Symposium (USENIX Security 22). 4525\u20134542."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3579856.3590334"},{"key":"e_1_3_3_2_34_2","first-page":"4579","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Mehnaz Shagufta","year":"2022","unstructured":"Shagufta Mehnaz, Sayanton\u00a0V Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino. 2022. Are your sensitive attributes private? Novel model inversion attribute inference attacks on classification models. In 31st USENIX Security Symposium (USENIX Security 22). 4579\u20134596."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00973"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00780"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Ahmed Salem Yang Zhang Humbert et\u00a0al. 2018. ML-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1806.01246 (2018).","DOI":"10.14722\/ndss.2019.23119"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417270"},{"key":"e_1_3_3_2_42_2","first-page":"2615","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Song Liwei","year":"2021","unstructured":"Liwei Song and Prateek Mittal. 2021. Systematic evaluation of privacy risks of machine learning models. In 30th USENIX Security Symposium (USENIX Security 21). 2615\u20132632."},{"key":"e_1_3_3_2_43_2","unstructured":"Igor Susmelj Matthias Heller Philipp Wirth Jeremy Prescott Malte Ebner and et al.2020. Lightly. https:\/\/github.com\/lightly-ai\/lightly [Accessed: 2025-01-01]."},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560554"},{"key":"e_1_3_3_2_45_2","unstructured":"Tobias Uelwer Jan Robine Stefan\u00a0Sylvius Wagner Marc H\u00f6ftmann Eric Upschulte Sebastian Konietzny Maike Behrendt and Stefan Harmeling. 2023. A Survey on Self-Supervised Representation Learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2308.11455 (2023)."},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3579856.3590339"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Zhaoxi Zhang Leo Yu\u00a0Zhang Xufei Zheng Bilal Hussain\u00a0Abbasi and Shengshan Hu. 2022. Evaluating membership inference through adversarial robustness. Comput. J. 65 11 (2022) 2969\u20132978.","DOI":"10.1093\/comjnl\/bxac080"},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom56396.2022.00137"},{"key":"e_1_3_3_2_51_2","unstructured":"Jinghao Zhou Chen Wei Huiyu Wang Wei Shen Cihang Xie Alan Yuille and Tao Kong. 2021. iBOT: Image BERT Pre-Training with Online Tokenizer. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2111.07832 (2021)."},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690202"},{"key":"e_1_3_3_2_53_2","unstructured":"Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep leakage from gradients. Advances in Neural Information Processing Systems 32 (2019)."}],"event":{"name":"ASIA CCS '25: 20th ACM Asia Conference on Computer and Communications Security","location":"Hanoi Vietnam","acronym":"ASIA CCS '25","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 20th ACM Asia Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708821.3733915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T07:25:51Z","timestamp":1755069951000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708821.3733915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,24]]},"references-count":52,"alternative-id":["10.1145\/3708821.3733915","10.1145\/3708821"],"URL":"https:\/\/doi.org\/10.1145\/3708821.3733915","relation":{},"subject":[],"published":{"date-parts":[[2025,8,24]]},"assertion":[{"value":"2025-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}