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SaTML 2023: Raleigh, NC, USA
- 2023 IEEE Conference on Secure and Trustworthy Machine Learning, SaTML 2023, Raleigh, NC, USA, February 8-10, 2023. IEEE 2023, ISBN 978-1-6654-6300-3
- Stefano Calzavara, Lorenzo Cazzaro
, Claudio Lucchese, Federico Marcuzzi
Explainable Global Fairness Verification of Tree-Based Classifiers. 1-17 - Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala:
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction. 18-41 - Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth
Wealth Dynamics Over Generations: Analysis and Interventions. 42-57 - Patrik Joslin Kenfack, Adín Ramírez Rivera, Adil Mehmood Khan, Manuel Mazzara:
Learning Fair Representations through Uniformly Distributed Sensitive Attributes. 58-67 - Guy Heller, Ethan Fetaya:
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning? 68-106 - Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning. 107-118 - Sayanton V. Dibbo, Dae Lim Chung, Shagufta Mehnaz:
Model Inversion Attack with Least Information and an In-depth Analysis of its Disparate Vulnerability. 119-135 - Valentin Hartmann, Léo Meynent, Maxime Peyrard, Dimitrios Dimitriadis, Shruti Tople, Robert West:
Distribution Inference Risks: Identifying and Mitigating Sources of Leakage. 136-149 - Anshuman Suri, Yifu Lu, Yanjin Chen, David Evans:
Dissecting Distribution Inference. 150-164 - Sanjay Kariyappa, Moinuddin K. Qureshi:
ExPLoit: Extracting Private Labels in Split Learning. 165-175 - Harsh Chaudhari, Matthew Jagielski, Alina Oprea:
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning. 176-196 - Huzaifa Arif, Alex Gittens, Pin-Yu Chen:
Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming. 197-209 - Rachel Cummings, Hadi Elzayn, Emmanouil Pountourakis, Vasilis Gkatzelis, Juba Ziani:
Optimal Data Acquisition with Privacy-Aware Agents. 210-224 - Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal:
A Light Recipe to Train Robust Vision Transformers. 225-253 - Washington Garcia, Pin-Yu Chen, Hamilton Scott Clouse, Somesh Jha, Kevin R. B. Butler:
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks. 254-270 - Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Publishing Efficient On-device Models Increases Adversarial Vulnerability. 271-290 - Xiaojun Xu, Hanzhang Wang, Alok Lal, Carl A. Gunter, Bo Li:
EDoG: Adversarial Edge Detection For Graph Neural Networks. 291-305 - Nishtha Madaan, Diptikalyan Saha, Srikanta Bedathur:
Counterfactual Sentence Generation with Plug-and-Play Perturbation. 306-315 - Minseon Kim, Jihoon Tack, Jinwoo Shin, Sung Ju Hwang:
Rethinking the Entropy of Instance in Adversarial Training. 316-326 - Fangcheng Liu, Chao Zhang, Hongyang Zhang:
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes. 327-338 - Giovanni Apruzzese
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. 339-364 - Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. 365-376 - Gorka Abad
Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning. 377-391 - Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James Bailey:
Backdoor Attacks on Time Series: A Generative Approach. 392-403 - Hojjat Aghakhani, Lea Schönherr, Thorsten Eisenhofer, Dorothea Kolossa, Thorsten Holz, Christopher Kruegel, Giovanni Vigna:
Venomave: Targeted Poisoning Against Speech Recognition. 404-417 - Patrick Altmeyer
Endogenous Macrodynamics in Algorithmic Recourse. 418-431 - Yingyan Zeng, Jiachen T. Wang, Si Chen, Hoang Anh Just, Ran Jin, Ruoxi Jia:
ModelPred: A Framework for Predicting Trained Model from Training Data. 432-449 - Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden:
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. 450-463 - Tilman Räuker, Anson Ho, Stephen Casper, Dylan Hadfield-Menell:
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. 464-483 - Zayd Hammoudeh, Daniel Lowd:
Reducing Certified Regression to Certified Classification for General Poisoning Attacks. 484-523 - Florian Jaeckle, M. Pawan Kumar:
Neural Lower Bounds for Verification. 524-536 - Haoze Wu
Toward Certified Robustness Against Real-World Distribution Shifts. 537-553 - Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li:
CARE: Certifiably Robust Learning with Reasoning via Variational Inference. 554-574 - Mintong Kang, Linyi Li, Bo Li:
FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust DNNs. 575-592 - Toluwani Aremu
PolyKervNets: Activation-free Neural Networks For Efficient Private Inference. 593-604 - Ari Karchmer:
Theoretical Limits of Provable Security Against Model Extraction by Efficient Observational Defenses. 605-621 - Korbinian Koch, Marcus Soll
No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes. 622-637 - Zhifeng Kong, Kamalika Chaudhuri:
Data Redaction from Pre-trained GANs. 638-677 - Teresa Datta, Daniel Nissani, Max Cembalest, Akash Khanna, Haley Massa, John Dickerson:
Tensions Between the Proxies of Human Values in AI. 678-689 - Amanda Coston, Anna Kawakami, Haiyi Zhu
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. 690-704
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