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Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact.
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Simpson’s paradox in psychological science: a practical guide
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The algorithmic foundations of differential privacy
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Sensitivity analysis in the context of regional safety modeling: Identifying and assessing the modifiable areal unit problem
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Big questions for social media big data: Representativeness, validity and other methodological pitfalls
Zeynep Tufekci · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Secure multi-party differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeff Dean, Matthieu Devin, et al · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Auror: Defending against poisoning attacks in collaborative deep learning systems
Shiqi Shen, Shruti Tople, and Prateek Saxena · 2016
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Oblivious { \{ Multi-Party } \} machine learning on trusted processors
Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa · 2016
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Stealing machine learning models via prediction { \{ APIs } \}
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Algorithms for differentially private multi-armed bandits
Aristide CY Tossou and Christos Dimitrakakis · 2016
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Distributed cooperative decision-making in multiarmed bandits: Frequentist and bayesian algorithms
Peter Landgren, Vaibhav Srivastava, and Naomi Ehrich Leonard · 2016
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Averaging gone wrong: Using time-aware analyses to better understand behavior
Samuel Barbosa, Dan Cosley, Amit Sharma, and Roberto M Cesar Jr · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Mitigating evasion attacks to deep neural networks via region-based classification
Xiaoyu Cao and Neil Zhenqiang Gong · 2017
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Adversarial example defense: Ensembles of weak defenses are not strong
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
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Learning from untrusted data
Moses Charikar, Jacob Steinhardt, and Gregory Valiant · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Privacy-preserving deep learning via additively homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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A formal foundation for secure remote execution of enclaves
Pramod Subramanyan, Rohit Sinha, Ilia Lebedev, Srinivas Devadas, and Sanjit A Seshia · 2017
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Achieving privacy in the adversarial multi-armed bandit
Aristide Charles Yedia Tossou and Christos Dimitrakakis · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Private and truthful aggregative game for large-scale spectrum sharing
Pan Zhou, Wenqi Wei, Kaigui Bian, Dapeng Oliver Wu, Yuchong Hu, and Qian Wang · 2017
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi · 2018
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Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2018
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Ai2: Safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
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A framework for evaluating client privacy leakages in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
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Inverting gradients - how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
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Ldp-fed: federated learning with local differential privacy
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei · 2020
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Stolen memories: Leveraging model memorization for calibrated { \{ White-Box } \} membership inference
Klas Leino and Matt Fredrikson · 2020
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High accuracy and high fidelity extraction of neural networks
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2020
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
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The hidden vulnerability of distributed learning in byzantium
Rachid Guerraoui, Sébastien Rouault, et al · 2018
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Sanjay Kariyappa and Moinuddin K Qureshi · 2020
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Prediction poisoning: Towards defenses against dnn model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2020
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Differentially-private federated linear bandits
Abhimanyu Dubey and AlexSandy’ Pentland · 2020
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Cooperative multi-agent bandits with heavy tails
Abhimanyu Dubey and Alex ‘Sandy’ Pentland · 2020
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Private and byzantine-proof cooperative decision-making
Abhimanyu Dubey and Alex Pentland · 2020
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Adapting to misspecification in contextual bandits
Dylan J Foster, Claudio Gentile, Mehryar Mohri, and Julian Zimmert · 2020
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Explainable and ethical ai: a perspective on argumentation and logic programming
Roberta Calegari, Andrea Omicini, and Giovanni Sartor · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
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Gradient-leakage resilient federated learning
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Gradient leakage attack resilient deep learning
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A survey on bias and fairness in machine learning
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Federated multi-armed bandits
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Boosting ensemble accuracy by revisiting ensemble diversity metrics
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Lomar: A local defense against poisoning attack on federated learning
Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao Liu · 2021
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Spectre: defending against backdoor attacks using robust statistics
Jonathan Hayase, Weihao Kong, Raghav Somani, and Sewoong Oh · 2021
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Demon in the variant: Statistical analysis of { \{ DNNs } \} for robust backdoor contamination detection
Di Tang, XiaoFeng Wang, Haixu Tang, and Kehuan Zhang · 2021
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Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
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Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2021
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Deep partition aggregation: Provable defense against general poisoning attacks
A Levine and S Feizi · 2021
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How robust are randomized smoothing based defenses to data poisoning?
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Robust learning for data poisoning attacks
Yunjuan Wang, Poorya Mianjy, and Raman Arora · 2021
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Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff
Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, and Arjun Gupta · 2021
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You autocomplete me: Poisoning vulnerabilities in neural code completion
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov · 2021
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Anti-backdoor learning: Training clean models on poisoned data
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Addressing class imbalance in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2021
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R-gap: Recursive gradient attack on privacy
Junyi Zhu and Matthew Blaschko · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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Evaluating gradient inversion attacks and defenses in federated learning
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, and Sanjeev Arora · 2021
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Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, and Michael Mitzenmacher · 2021
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Cafe: Catastrophic data leakage in vertical federated learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen · 2021
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Ldp-fl: Practical private aggregation in federated learning with local differential privacy
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Federated f-differential privacy
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Ppfl: privacy-preserving federated learning with trusted execution environments
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Soteria: Provable defense against privacy leakage in federated learning from representation perspective
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The tsc-pfed architecture for privacy-preserving fl
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Against membership inference attack: Pruning is all you need
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Feature inference attack on model predictions in vertical federated learning
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Unleashing the tiger: Inference attacks on split learning
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Entangled watermarks as a defense against model extraction
Hengrui Jia, Christopher A Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot · 2021
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Privacy-enhanced federated learning against poisoning adversaries
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Multiplayer bandit learning, from competition to cooperation
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Towards a digital ecosystem of trust: Ethical, legal and societal implications
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Federated learning used for predicting outcomes in sars-cov-2 patients
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Machine unlearning
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Ethical and social risks of harm from language models
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Adversarial resilient and privacy preserving deep learning
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Stronger data poisoning attacks break data sanitization defenses
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An agnostic approach to federated learning with class imbalance
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Gradient obfuscation gives a false sense of security in federated learning
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Lamp: Extracting text from gradients with language model priors
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Label inference attacks against vertical federated learning
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Defense against gradient leakage attacks via learning to obscure data
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Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning
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Flame: Taming backdoors in federated learning
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Shieldfl: Mitigating model poisoning attacks in privacy-preserving federated learning
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A decision-support framework for data anonymization with application to machine learning processes
Loredana Caruccio, Domenico Desiato, Giuseppe Polese, Genoveffa Tortora, and Nicola Zannone · 2022
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High-resolution image synthesis with latent diffusion models
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Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro · 2022
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Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning
Wei Du, Yichun Zhao, Boqun Li, Gongshen Liu, and Shilin Wang · 2022
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Stdlens: Model hijacking-resilient federated learning for object detection
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Exploring model learning heterogeneity for boosting ensemble robustness
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, and Ling Liu · 2023
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Model cloaking against gradient leakage
Wenqi Wei, Ka-Ho Chow, Fatih Ilhan, Yanzhao Wu, and Ling Liu · 2023
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Securing distributed sgd against gradient leakage threats
Wenqi Wei, Ling Liu, Jingya Zhou, Ka-Ho Chow, and Yanzhao Wu · 2023
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Machine learning for synthetic data generation: a review
Yingzhou Lu, Huazheng Wang, and Wenqi Wei · 2023
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OpenAI · 2023
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Extracting training data from diffusion models
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Gradient coupling effect of poisoning attacks in federated learning
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