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The prosperity of machine learning has also brought people's concerns about data privacy.
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BlurMe: Inferring and obfuscating user gender based on ratings. In Proceedings of the sixth ACM conference on Recommender systems . 195–202
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Recommending with an agenda: Active learning of private attributes using matrix factorization. In Proceedings of the 8th ACM conference on recommender systems . 65–72
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Privacy in Pharmacogenetics: An { \{ End-to-End } \} Case Study of Personalized Warfarin Dosing. In 23rd USENIX Security Symposium (USENIX Security 14) . 17–32
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Biometric antispoofing methods: A survey in face recognition
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You are what you watch and when you watch: Inferring household structures from IPTV viewing data
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Towards making systems forget with machine unlearning. In 2015 IEEE Symposium on Security and Privacy . IEEE, 463–480
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FHEW: bootstrapping homomorphic encryption in less than a second. In Annual international conference on the theory and applications of cryptographic techniques . Springer, 617–640
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Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . 1322–1333
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Machine learning: Trends, perspectives, and prospects
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Inference attacks on property-preserving encrypted databases. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . 644–655
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Mlaas: Machine learning as a service. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) . IEEE, 896–902
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Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security . 308–318
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Collective data-sanitization for preventing sensitive information inference attacks in social networks
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You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors. In 25th USENIX Security Symposium (USENIX Security 16) . 979–995
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Learning privately from multiparty data. In International Conference on Machine Learning . PMLR, 555–563
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Improving attribute inference attack using link prediction in online social networks
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Autoencoding beyond pixels using a learned similarity metric. In International conference on machine learning . PMLR, 1558–1566
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The limitations of deep learning in adversarial settings. In 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 372–387
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Stealing Machine Learning Models via Prediction { \{ APIs } \} . In 25th USENIX security symposium (USENIX Security 16) . 601–618
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On the protection of private information in machine learning systems: Two recent approches. In 2017 IEEE 30th Computer Security Foundations Symposium (CSF) . IEEE, 1–6
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Interpreting blackbox models via model extraction
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Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp) . IEEE, 39–57
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Generating multi-label discrete patient records using generative adversarial networks. In Machine learning for healthcare conference . PMLR, 286–305
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Inductive representation learning on large graphs
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Logan: Evaluating information leakage of generative models using generative adversarial networks
Jamie Hayes, Luca Melis, George Danezis, and ED Cristofaro. 2017 · 2017
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Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes. In 2017 15th Annual Conference on Privacy, Security and Trust (PST) . IEEE, 115–11509
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Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security . 603–618
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Reluplex: An efficient SMT solver for verifying deep neural networks. In International conference on computer aided verification . Springer, 97–117
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Scaling machine learning as a service. In International Conference on Predictive Applications and APIs . PMLR, 14–29
Li Erran Li, Eric Chen, Jeremy Hermann, Pusheng Zhang, and Luming Wang. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017b · 2017
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How to steal a machine learning classifier with deep learning. In 2017 IEEE International symposium on technologies for homeland security (HST) . IEEE, 1–5
Yi Shi, Yalin Sagduyu, and Alexander Grushin. 2017 · 2017
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Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP) . IEEE, 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Machine learning models that remember too much. In Proceedings of the 2017 ACM SIGSAC Conference on computer and communications security . 587–601
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017 · 2017
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Deep sets
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Stealing neural networks via timing side channels
Vasisht Duddu, Debasis Samanta, D Vijay Rao, and Valentina E Balas. 2018 · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations. In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security . 619–633
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Attribute inference attacks in online social networks
Neil Zhenqiang Gong and Bin Liu. 2018 · 2018
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Privacy-preserving machine learning as a service
Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi, and Rebecca N Wright. 2018 · 2018
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Chiron: Privacy-preserving machine learning as a service
Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, and Emmett Witchel. 2018 · 2018
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User data privacy: Facebook, Cambridge Analytica, and privacy protection
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{ \{ AttriGuard } \} : A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning. In 27th USENIX Security Symposium (USENIX Security 18) . 513–529
Jinyuan Jia and Neil Zhenqiang Gong. 2018 · 2018
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Model extraction warning in mlaas paradigm. In Proceedings of the 34th Annual Computer Security Applications Conference . 371–380
Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya, and Sameep Mehta. 2018 · 2018
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Nsml: Meet the mlaas platform with a real-world case study
Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, et al · 2018
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Differentially Private Recommendation System Based on Community Detection in Social Network Applications
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Understanding membership inferences on well-generalized learning models
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Digital watermarking for deep neural networks
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Machine learning with membership privacy using adversarial regularization. In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security . 634–646
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2018 · 2018
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Private machine learning classification based on fully homomorphic encryption
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Stealing hyperparameters in machine learning. In 2018 IEEE symposium on security and privacy (SP) . IEEE, 36–52
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
One for one, or all for all: Equilibria and optimality of collaboration in federated learning. In International Conference on Machine Learning . PMLR, 1005–1014
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Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) . 2633–2650
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When machine unlearning jeopardizes privacy. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 896–911
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Dataset correlation inference attacks against machine learning models
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Cited alongside, same era.
Synthesizing tabular data using generative adversarial networks
Lei Xu and Kalyan Veeramachaneni. 2018 · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF) . IEEE, 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
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Gamin: An adversarial approach to black-box model inversion
Ulrich Aïvodji, Sébastien Gambs, and Timon Ther. 2019 · 2019
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Gender inference for Facebook picture owners. In International Conference on Trust and Privacy in Digital Business . Springer, 145–160
Bizhan Alipour, Abdessamad Imine, and Michaël Rusinowitch. 2019 · 2019
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Amazon Rekognition
Amazon. 2019 · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19) . 267–284
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Adversarial examples are a natural consequence of test error in noise. In International Conference on Machine Learning . PMLR, 2280–2289
Justin Gilmer, Nicolas Ford, Nicholas Carlini, and Ekin Cubuk. 2019 · 2019
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Tiantian Feng, Hanieh Hashemi, Rajat Hebbar, Murali Annavaram, and Shrikanth S Narayanan. 2021 · 2021
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Membership inference attacks on deep regression models for neuroimaging. In Medical Imaging with Deep Learning . PMLR, 228–251
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Prid: Model inversion privacy attacks in hyperdimensional learning systems. In 2021 58th ACM/IEEE Design Automation Conference (DAC) . IEEE, 553–558
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Membership inference attacks on machine learning: A survey
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Evaluating the Vulnerability of End-to-End Automatic Speech Recognition Models to Membership Inference Attacks.. In Interspeech . 891–895
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Variational Model Inversion Attacks
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Data Privacy Protection based on Feature Dilution in Cloud Services. In 2021 IEEE Global Communications Conference (GLOBECOM) . IEEE, 1–6
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Membership inference attacks against recommender systems. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 864–879
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TEA-RNN: Topic-Enhanced Attentive RNN for Attribute Inference Attacks via User Behaviors. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) . IEEE, 174–179
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Graphmi: Extracting private graph data from graph neural networks
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On the (in) feasibility of attribute inference attacks on machine learning models. In 2021 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 232–251
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Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Jan Aalmoes, Vasisht Duddu, and Antoine Boutet. 2022 · 2022
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Label-Only Membership Inference Attack against Node-Level Graph Neural Networks. In Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security . 1–12
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QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 623–637
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Leveraging Adversarial Examples to Quantify Membership Information Leakage. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10399–10409
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Gradvit: Gradient inversion of vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10021–10030
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Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning. In European Conference on Computer Vision . Springer, 365–381
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Label-Only Model Inversion Attacks via Boundary Repulsion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 15045–15053
Mostafa Kahla, Si Chen, Hoang Anh Just, and Ruoxi Jia. 2022 · 2022
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Model Inversion Attack by Integration of Deep Generative Models: Privacy-Sensitive Face Generation from a Face Recognition System
Mahdi Khosravy, Kazuaki Nakamura, Yuki Hirose, Naoko Nitta, and Noboru Babaguchi. 2022 · 2022
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Privacy-preserving machine learning with fully homomorphic encryption for deep neural network
Joon-Woo Lee, HyungChul Kang, Yongwoo Lee, Woosuk Choi, Jieun Eom, Maxim Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, et al · 2022
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User-Level Membership Inference Attack against Metric Embedding Learning. In ICLR 2022 Workshop on PAIR
Guoyao Li, Shahbaz Rezaei, and Xin Liu. 2022 · 2022
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Membership Inference Attacks Against Machine Learning Models via Prediction Sensitivity
Lan Liu, Yi Wang, Gaoyang Liu, Kai Peng, and Chen Wang. 2022 · 2022
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Property Inference from Poisoning. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, 1569–1569
Saeed Mahloujifar, Esha Ghosh, and Melissa Chase. 2022 · 2022
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Shagufta Mehnaz, Sayanton V Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino. 2022 · 2022
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Private graph extraction via feature explanations
Iyiola E Olatunji, Mandeep Rathee, Thorben Funke, and Megha Khosla. 2022 · 2022
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Membership inference attack and defense for wireless signal classifiers with deep learning
Yi Shi and Yalin Sagduyu. 2022 · 2022
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Subject Membership Inference Attacks in Federated Learning
Anshuman Suri, Pallika Kanani, Virendra J Marathe, and Daniel W Peterson. 2022 · 2022
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Beyond gradients: Exploiting adversarial priors in model inversion attacks
Dmitrii Usynin, Daniel Rueckert, and Georgios Kaissis. 2022 · 2022
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Exploring the vulnerability in the inference phase of advanced persistent threats
Qi Wu, Qiang Li, Dong Guo, and Xiangyu Meng. 2022a · 2022
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Membership Inference Attacks Against Text-to-image Generation Models
Yixin Wu, Ning Yu, Zheng Li, Michael Backes, and Yang Zhang. 2022c · 2022
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Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
Dayong Ye, Huiqiang Chen, Shuai Zhou, Tianqing Zhu, Wanlei Zhou, and Shouling Ji. 2022a · 2022
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Label-only Model Inversion Attack: The Attack that Requires the Least Information
Dayong Ye, Tianqing Zhu, Shuai Zhou, Bo Liu, and Wanlei Zhou. 2022b · 2022
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Membership Inference Attacks and Defenses in Neural Network Pruning
Xiaoyong Yuan and Lan Zhang. 2022 · 2022
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Inference attacks against graph neural networks. In Proceedings of the 31th USENIX Security Symposium . 1–18
Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, and Yang Zhang. 2022 · 2022
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Linkteller: Recovering private edges from graph neural networks via influence analysis. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2005–2024
Fan Wu, Yunhui Long, Ce Zhang, and Bo Li. 2022b · 2024
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