{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:18:59Z","timestamp":1730297939342,"version":"3.28.0"},"reference-count":67,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-009"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-001"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/sp46215.2023.10179409","type":"proceedings-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T17:18:15Z","timestamp":1689959895000},"page":"2170-2189","source":"Crossref","is-referenced-by-count":2,"title":["A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction"],"prefix":"10.1109","author":[{"given":"Hanshen","family":"Xiao","sequence":"first","affiliation":[{"name":"MIT"}]},{"given":"Zihang","family":"Xiang","sequence":"additional","affiliation":[{"name":"KAUST"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"KAUST"}]},{"given":"Srinivas","family":"Devadas","sequence":"additional","affiliation":[{"name":"MIT"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"article-title":"Machine learning model sizes and the parameter gap","year":"2022","author":"Villalobos","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref6","first-page":"14774","article-title":"Deep leakage from gradients","volume":"32","author":"Zhu","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","first-page":"22205","article-title":"Auditing differentially private machine learning: How private is private sgd?","volume":"33","author":"Jagielski","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"article-title":"Differentially private learning needs better features (or much more data)","volume-title":"International Conference on Learning Representations","author":"Tramer","key":"ref9"},{"article-title":"Making the shoe fit: Architectures, initializations, and tuning for learning with privacy","year":"2019","author":"Papernot","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453949"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref17","first-page":"28","article-title":"Nearly optimal private lasso","author":"Talwar","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref19","first-page":"2722","article-title":"Differentially private empirical risk minimization revisited: Faster and more general","author":"Wang","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"first-page":"10081","article-title":"On differentially private stochastic convex optimization with heavy-tailed data","volume-title":"International Conference on Machine Learning","author":"Wang","key":"ref20"},{"journal-title":"ACM SIGMOD PODS","article-title":"High dimensional differentially private stochastic optimization with heavy-tailed data","year":"2021","author":"Hu","key":"ref21"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.35"},{"article-title":"Toward training at imagenet scale with differential privacy","year":"2022","author":"Kurakin","key":"ref23"},{"article-title":"Fine-tuning with differential privacy necessitates an additional hyperparameter search","year":"2022","author":"Cattan","key":"ref24"},{"article-title":"Automatic clipping: Differentially private deep learning made easier and stronger","year":"2022","author":"Bu","key":"ref25"},{"key":"ref26","first-page":"31","article-title":"How does batch normalization help optimization?","author":"Santurkar","year":"2018","journal-title":"Advances in neural information processing systems"},{"article-title":"Do not let privacy overbill utility: Gradient embedding perturbation for private learning","volume-title":"International Conference on Learning Representations","author":"Yu","key":"ref27"},{"article-title":"Unlocking high-accuracy differentially private image classification through scale","year":"2022","author":"De","key":"ref28"},{"issue":"3","key":"ref29","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"Journal of Machine Learning Research"},{"article-title":"Semi-supervised knowledge transfer for deep learning from private training data","year":"2016","author":"Papernot","key":"ref30"},{"article-title":"Scalable private learning with pate","year":"2018","author":"Papernot","key":"ref31"},{"article-title":"Why gradient clipping accelerates training: A theoretical justification for adaptivity","volume-title":"International Conference on Learning Representations","author":"Zhang","key":"ref32"},{"key":"ref33","first-page":"13773","article-title":"Understanding gradient clipping in private sgd: A geometric perspective","volume":"33","author":"Chen","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Characterizing private clipped gradient descent on convex generalized linear problems","year":"2020","author":"Song","key":"ref34"},{"article-title":"Normalized\/clipped sgd with perturbation for differentially private non-convex optimization","year":"2022","author":"Yang","key":"ref35"},{"article-title":"Study on the large batch size training of neural networks based on the second order gradient","year":"2020","author":"Gao","key":"ref36"},{"key":"ref37","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume":"34","author":"Andrew","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"first-page":"12208","article-title":"Large scale private learning via low-rank reparametrization","volume-title":"International Conference on Machine Learning","author":"Yu","key":"ref38"},{"article-title":"Bypassing the ambient dimension: Private sgd with gradient subspace identification","volume-title":"International Conference on Learning Representations","author":"Zhou","key":"ref39"},{"first-page":"383","article-title":"Private adaptive gradient methods for convex optimization","volume-title":"International Conference on Machine Learning","author":"Asi","key":"ref40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00502"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3313276.3316377"},{"article-title":"Hyperparameter tuning with renyi differential privacy","year":"2021","author":"Papernot","key":"ref43"},{"journal-title":"Journal of Machine Learning Research","article-title":"Differentially private learning with kernels","year":"2013","author":"Jain","key":"ref44"},{"article-title":"R\\\u2019enyi differential privacy of the sampled gaussian mechanism","year":"2019","author":"Mironov","key":"ref45"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.12"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"International conference on machine learning","author":"Ioffe","key":"ref48"},{"key":"ref49","first-page":"32","article-title":"Momentum-based variance reduction in non-convex sgd","author":"Cutkosky","year":"2019","journal-title":"Advances in neural information processing systems"},{"article-title":"A general approach to adding differential privacy to iterative training procedures","year":"2018","author":"McMahan","key":"ref50"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1002\/wics.56"},{"first-page":"2525","article-title":"Not all samples are created equal: Deep learning with importance sampling","volume-title":"International conference on machine learning","author":"Katharopoulos","key":"ref52"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560562"},{"article-title":"Recent advances in recurrent neural networks","year":"2017","author":"Salehinejad","key":"ref54"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"volume-title":"Deep learning","year":"2016","author":"Goodfellow","key":"ref56"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"article-title":"Layer normalization","year":"2016","author":"Ba","key":"ref58"},{"article-title":"Micro-batch training with batch-channel normalization and weight standardization","year":"2019","author":"Qiao","key":"ref59"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2855738"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7502-7_79-1"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref65"},{"journal-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"Netzer","key":"ref66"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.5555\/2002472.2002491"}],"event":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","start":{"date-parts":[[2023,5,21]]},"location":"San Francisco, CA, USA","end":{"date-parts":[[2023,5,25]]}},"container-title":["2023 IEEE Symposium on Security and Privacy (SP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10179215\/10179280\/10179409.pdf?arnumber=10179409","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T05:15:46Z","timestamp":1721452546000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10179409\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1109\/sp46215.2023.10179409","relation":{},"subject":[],"published":{"date-parts":[[2023,5]]}}}
