GONE: A generic O(1) NoisE layer for protecting privacy of deep neural networks

Computers & Security - Tập 135 - Trang 103471 - 2023
Haibin Zheng1, Jinyin Chen1, Wenchang Shangguan1, Zhaoyan Ming2, Xing Yang3, Zhijun Yang4
1Zhejiang University of Technology, Hangzhou, 310023, China
2Hangzhou City University, Hangzhou 310015, China
3Electronic Engineering Institute, National University of Defense Technology, Hefei, 230037, China
4Jack Technology Co., Ltd., Taizhou, 318099, China

Tài liệu tham khảo

Abadi, 2016, Deep learning with differential privacy, 308 Agarwal, 2021, Damad: Database, attack, and model agnostic adversarial perturbation detector, IEEE Trans. Neural Netw. Learn. Syst., 1 Agarwal, 2021, Image transformation-based defense against adversarial perturbation on deep learning models, IEEE Trans. Dependable Secure Comput., 18, 2106 Athalye, 2018, Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples, 274 Backes, 2016, Membership privacy in microrna-based studies, 319 Bondielli, 2019, A survey on fake news and rumour detection techniques, Inf. Sci., 497, 38, 10.1016/j.ins.2019.05.035 Brendel, 2018, Decision-based adversarial attacks: Reliable attacks against black-box machine learning models, 1 Bulò, 2017, Randomized prediction games for adversarial machine learning, IEEE Trans. Neural Netw. Learn. Syst., 28, 2466, 10.1109/TNNLS.2016.2593488 Carlini, 2016, Towards evaluating the robustness of neural networks, 39 Che Chen, 2020, Hopskipjumpattack: A query-efficient decision-based attack, 1277 Chen, 2019, POBA-GA: perturbation optimized black-box adversarial attacks via genetic algorithm, Comput. Secur., 85, 89, 10.1016/j.cose.2019.04.014 Chen, 2017, ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models, 15 Choquette-Choo, 2021, Label-only membership inference attacks, 1964 Cohen, 2019, Certified adversarial robustness via randomized smoothing, 1310 Dong, 2018, Boosting adversarial attacks with momentum, 9185 Dwork, 2014, The algorithmic foundations of differential privacy, Found. Trends Theor. Comput. Sci., 9, 211, 10.1561/0400000042 Eom, 2020, Effective privacy preserving data publishing by vectorization, Inf. Sci., 527, 311, 10.1016/j.ins.2019.09.035 Geng, 2022, Novel target attention convolutional neural network for relation classification, Inf. Sci., 597, 24, 10.1016/j.ins.2022.03.024 Goodfellow, 2014, Explaining and harnessing adversarial examples, 1 Guo, 2018, Countering adversarial images using input transformations, 1 He, 2016, Deep residual learning for image recognition, 770 He, 2016, Identity mappings in deep residual networks, 630 Hosseini, 2019, Dropping pixels for adversarial robustness, 91 Ilyas, 2018, Black-box adversarial attacks with limited queries and information, 2142 Jeddi, 2020, Learn2perturb: An end-to-end feature perturbation learning to improve adversarial robustness, 1238 Jia, 2019, Memguard: Defending against black-box membership inference attacks via adversarial examples, 259 Juuti, 2019, PRADA: protecting against DNN model stealing attacks, 512 Kesarwani, 2018, Model extraction warning in mlaas paradigm, 371 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1106 Kurakin, 2017, Adversarial machine learning at scale, 1 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 LeCun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541, 10.1162/neco.1989.1.4.541 Lécuyer, 2019, Certified robustness to adversarial examples with differential privacy, 656 Lee Li, 2021, Deep learning for lidar point clouds in autonomous driving: A review, IEEE Trans. Neural Netw. Learn. Syst., 32, 3412, 10.1109/TNNLS.2020.3015992 Liu, 2018, Towards robust neural networks via random self-ensemble, 381 Liu, 2021, Speech emotion recognition based on formant characteristics feature extraction and phoneme type convergence, Inf. Sci., 563, 309, 10.1016/j.ins.2021.02.016 Lowd, 2005, Adversarial learning, 641 Van der Maaten, 2008, Visualizing data using t-sne, J. Mach. Learn. Res., 9, 1 Nasr, 2018, Machine learning with membership privacy using adversarial regularization, 634 Orekondy, 2019, Knockoff nets: Stealing functionality of black-box models, 4954 Orekondy, 2020, Prediction poisoning: Towards defenses against DNN model stealing attacks, 1 Pajola, 2021, Fall of giants: How popular text-based mlaas fall against a simple evasion attack, 198 Pan, 2021, PNAS: A privacy preserving framework for neural architecture search services, Inf. Sci., 573, 370, 10.1016/j.ins.2021.05.073 Papernot, 2017, Practical black-box attacks against machine learning, 506 Phan, 2017, Adaptive laplace mechanism: Differential privacy preservation in deep learning, 385 Pyrgelis, 2018, Knock knock, who's there? membership inference on aggregate location data, 1 Qian, 2020, Privacy-preserving based task allocation with mobile edge clouds, Inf. Sci., 507, 288, 10.1016/j.ins.2019.07.092 Sablayrolles, 2019, White-box vs black-box: Bayes optimal strategies for membership inference, 5558 Salem, 2019, Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models, 1 Salman, 2020, Denoised smoothing: A provable defense for pretrained classifiers, 1 Shi, 2019, Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification, Inf. Sci., 490, 1, 10.1016/j.ins.2019.03.055 Shokri, 2015, Privacy-preserving deep learning, 1310 Shokri, 2017, Membership inference attacks against machine learning models, 3 Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, 1 Strauss Su, 2019, One pixel attack for fooling deep neural networks, IEEE Trans. Evol. Comput., 23, 828, 10.1109/TEVC.2019.2890858 Szegedy, 2014, Intriguing properties of neural networks, 1 Torfi, 2022, Differentially private synthetic medical data generation using convolutional gans, Inf. Sci., 586, 485, 10.1016/j.ins.2021.12.018 Tramèr, 2016, Stealing machine learning models via prediction apis, 601 Tu, 2019, Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks, 742 Xie, 2017, Adversarial examples for semantic segmentation and object detection, 1378 Xie, 2019, Feature denoising for improving adversarial robustness, 501 Yu, 2018, Convolutional networks with cross-layer neurons for image recognition, Inf. Sci., 433–434, 241, 10.1016/j.ins.2017.12.045 Yuan, 2019, Adversarial examples: Attacks and defenses for deep learning, IEEE Trans. Neural Netw. Learn. Syst., 30, 2805, 10.1109/TNNLS.2018.2886017 Zheng, 2019, BDPL: A boundary differentially private layer against machine learning model extraction attacks, 66