{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T19:48:48Z","timestamp":1784404128012,"version":"3.55.0"},"reference-count":73,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-009"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-001"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1109\/sp46214.2022.9833649","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T15:28:05Z","timestamp":1658935685000},"page":"1897-1914","source":"Crossref","is-referenced-by-count":413,"title":["Membership Inference Attacks From First Principles"],"prefix":"10.1109","author":[{"given":"Nicholas","family":"Carlini","sequence":"first","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Chien","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milad","family":"Nasr","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Song","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Terzis","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Tram\u00e8r","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref57","author":"salem","year":"2018","journal-title":"ML-Leaks Model and data independent membership inference attacks and defenses on machine learning models"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-060116-054123"},{"key":"ref56","first-page":"5558","article-title":"White-box vs black-box: Bayes optimal strategies for membership inference","author":"sablayrolles","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref15","article-title":"What neural networks memorize and why: Discovering the long tail via influence estimation","author":"feldman","year":"2020","journal-title":"arXiv preprint arXiv 2008 00863"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/ng.436"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3357713.3384290"},{"key":"ref58","first-page":"1291","article-title":"Updates-leak: Data set inference and reconstruction attacks in online learning","author":"salem","year":"2020","journal-title":"29th USENIX Security Symposium (USENIX Security 20)"},{"key":"ref53","article-title":"Language models are unsupervised multitask learners","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref52","article-title":"who’s there? membership inference on aggregate location data","author":"pyrgelis","year":"2017","journal-title":"arXiv preprint arXiv 1708 06578"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2015.46"},{"key":"ref55","first-page":"61","article-title":"Membership inference attack against differentially private deep learning model","volume":"11","author":"rahman","year":"2018","journal-title":"Trans Data Priv"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref54","article-title":"Sampling attacks: Amplification of membership inference attacks by repeated queries","author":"rahimian","year":"2020","journal-title":"arXiv preprint arXiv 2009 08003"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243834"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref19","author":"he","year":"2015","journal-title":"Deep residual learning for image recognition"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0008"},{"key":"ref51","author":"phan","year":"2021","journal-title":"huyvnphan\/pytorch cifar10"},{"key":"ref50","article-title":"Scalable private learning with PATE","author":"papernot","year":"2018","journal-title":"arXiv preprint arXiv 1802 08908"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1098\/rsta.1933.0009","article-title":"On the problem of the most efficient tests of statistical hypotheses","volume":"231","author":"neyman","year":"1933","journal-title":"Philosophical Transactions of the Royal Society of London"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00069"},{"key":"ref42","article-title":"ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning","author":"murakonda","year":"2020","journal-title":"arXiv preprint arXiv 2007 09948"},{"key":"ref41","first-page":"28","article-title":"Spam filtering with naive Bayes–which naive Bayes?","volume":"17","author":"metsis","year":"2006","journal-title":"CEAS"},{"key":"ref44","first-page":"2287","article-title":"Quantifying the privacy risks of learning high-dimensional graphical models","author":"murakonda","year":"2021","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref43","first-page":"arxiv","article-title":"Ultimate power of inference attacks: Privacy risks of learning high-dimensional graphical models","author":"murakonda","year":"2019","journal-title":"ArXiv e-prints"},{"key":"ref49","first-page":"95","article-title":"SpamCop: A spam classification & organization program","author":"pantel","year":"1998","journal-title":"Proceedings of AAAI-98 Workshop on Learning for Text Categorization"},{"key":"ref8","article-title":"CINIC-10 is not Imagenet or CIFAR-10","author":"darlow","year":"2018","journal-title":"arXiv preprint arXiv 1810 03487"},{"key":"ref7","author":"cubuk","year":"2018","journal-title":"Autoaugment Learning augmentation policies from data"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref4","article-title":"Extracting training data from large language models","author":"carlini","year":"2021","journal-title":"30th USENIX Security Symposium (USENIX Security 21)"},{"key":"ref3","first-page":"267","article-title":"The secret sharer: Evaluating and testing unintended memorization in neural networks","author":"carlini","year":"2019","journal-title":"28th USENIX Security Symposium (USENIX Security 19)"},{"key":"ref6","first-page":"1964","article-title":"Label-only membership inference attacks","author":"choquette-choo","year":"2021","journal-title":"International Conference on Machine Learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330723"},{"key":"ref40","article-title":"Pointer sentinel mixture models","author":"merity","year":"2016","journal-title":"arXiv preprint arXiv 1609 07843"},{"key":"ref35","article-title":"ML-Doctor: Holistic risk assessment of inference attacks against machine learning models","author":"liu","year":"2021","journal-title":"arXiv preprint arXiv 2102 02888"},{"key":"ref34","article-title":"Membership leakage in label-only exposures","author":"li","year":"2020","journal-title":"arXiv preprint arXiv 2007 15256"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP48549.2020.00040"},{"key":"ref36","article-title":"Towards measuring membership privacy","author":"long","year":"2017","journal-title":"arXiv preprint arXiv 1712 09136"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972733.3"},{"key":"ref30","first-page":"950","article-title":"A simple weight decay can improve generalization","author":"krogh","year":"1992","journal-title":"Advances in neural information processing systems"},{"key":"ref33","article-title":"Stolen memories: Leveraging model memorization for calibrated white-box membership inference","author":"leino","year":"2019","journal-title":"arXiv preprint arXiv 1906 10026"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3406325.3451131"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref38","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"loshchilov","year":"2016","journal-title":"arXiv preprint arXiv 1608 03983"},{"key":"ref71","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"arXiv preprint arXiv 1605 07146"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref24","article-title":"Auditing differentially private machine learning: How private is private SGD?","author":"jagielski","year":"2020","journal-title":"arXiv preprint arXiv 2006 07529"},{"key":"ref68","article-title":"On the importance of difficulty calibration in membership inference attacks","author":"watson","year":"2021","journal-title":"arXiv preprint arXiv 2111 02269"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(88)90003-2"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1198\/10618600152418584"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363201"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0031"},{"key":"ref69","article-title":"Enhanced membership inference attacks against machine learning models","author":"ye","year":"2021","journal-title":"arXiv preprint arXiv 2111 02269"},{"key":"ref20","article-title":"Stealing links from graph neural networks","author":"he","year":"2021","journal-title":"30th USENIX Security Symposium (USENIX Security 21)"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref63","author":"song","year":"2020","journal-title":"Introducing a new privacy testing library in tensorflow"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1000167"},{"key":"ref66","article-title":"Towards demystifying membership inference attacks","author":"truex","year":"2018","journal-title":"arXiv preprint arXiv 1807 09173"},{"key":"ref21","first-page":"469","article-title":"Detecting credential spearphishing in enterprise settings","author":"ho","year":"2017","journal-title":"26th USENIX Security Symposium (USENIX Security 17)"},{"key":"ref65","author":"steinke","year":"0","journal-title":"The pitfalls of averagecase differential privacy"},{"key":"ref28","article-title":"Learning to detect and classify malicious executables in the wild","volume":"7","author":"kolter","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2808769.2808780"},{"key":"ref29","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref60","article-title":"Membership inference attacks against machine learning models","author":"shokri","year":"2016","journal-title":"arXiv preprint arXiv 1610 05729"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354211"},{"key":"ref61","article-title":"Systematic evaluation of privacy risks of machine learning models","author":"song","year":"2021","journal-title":"30th USENIX Security Symposium (USENIX Security 21)"}],"event":{"name":"2022 IEEE Symposium on Security and Privacy (SP)","location":"San Francisco, CA, USA","start":{"date-parts":[[2022,5,22]]},"end":{"date-parts":[[2022,5,26]]}},"container-title":["2022 IEEE Symposium on Security and Privacy (SP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9833550\/9833558\/09833649.pdf?arnumber=9833649","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T18:27:52Z","timestamp":1699468072000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9833649\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/sp46214.2022.9833649","relation":{},"subject":[],"published":{"date-parts":[[2022,5]]}}}