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ORCIDSaTML 2024: Toronto, ON, Canada
- IEEE Conference on Secure and Trustworthy Machine Learning, SaTML 2024, Toronto, ON, Canada, April 9-11, 2024. IEEE 2024, ISBN 979-8-3503-4950-4
- Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala:
Probabilistic Dataset Reconstruction from Interpretable Models. 1-17 - Zhangheng Li, Junyuan Hong, Bo Li, Zhangyang Wang:
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk. 18-32 - Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. 33-56 - Amol Khanna, Edward Raff, Nathan Inkawhich:
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data. 57-77 - Achraf Azize, Debabrota Basu:
Concentrated Differential Privacy for Bandits. 78-109 - Francesco Pinto
, Yaxi Hu, Fanny Yang, Amartya Sanyal:
PILLAR: How to make semi-private learning more effective. 110-139 - Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Fair Federated Learning via Bounded Group Loss. 140-160 - Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho:
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. 161-193 - Lukas Fluri, Daniel Paleka, Florian Tramèr:
Evaluating Superhuman Models with Consistency Checks. 194-232 - Chenxi Yang, Greg Anderson, Swarat Chaudhuri:
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation. 233-251 - Ashutosh Nirala, Ameya Joshi, Soumik Sarkar, Chinmay Hegde:
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing. 252-271 - Ruinan Jin, Chun-Yin Huang, Chenyu You, Xiaoxiao Li:
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP. 272-285 - Quentin Le Roux, Kassem Kallas, Teddy Furon:
REStore: Exploring a Black-Box Defense against DNN Backdoors using Rare Event Simulation. 286-308 - Hiroya Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima:
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning. 309-326 - Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. 327-343 - Eleanor Clifford, Ilia Shumailov, Yiren Zhao, Ross J. Anderson, Robert D. Mullins:
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks. 344-357 - Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie:
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models. 358-386 - Fnu Suya, Anshuman Suri, Tingwei Zhang, Jingtao Hong, Yuan Tian, David Evans:
SoK: Pitfalls in Evaluating Black-Box Attacks. 387-407 - Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr:
Evading Black-box Classifiers Without Breaking Eggs. 408-424 - Francesco Croce, Matthias Hein:
Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models. 425-442 - Chulin Xie, Pin-Yu Chen, Qinbin Li, Arash Nourian, Ce Zhang, Bo Li:
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM. 443-471 - Tatsuki Koga, Kamalika Chaudhuri, David Page:
Differentially Private Multi-Site Treatment Effect Estimation. 472-489 - Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai:
ScionFL: Efficient and Robust Secure Quantized Aggregation. 490-511 - Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. 512-533 - Ivoline C. Ngong, Nicholas Gibson, Joseph P. Near:
Olympia: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols. 534-551 - Andrew Geng, Pin-Yu Chen:
Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders. 552-568 - Zhifeng Kong, Kamalika Chaudhuri:
Data Redaction from Conditional Generative Models. 569-591 - Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng:
Towards Scalable and Robust Model Versioning. 592-611 - Abeba Birhane, Ryan Steed, Victor Ojewale, Briana Vecchione, Inioluwa Deborah Raji:
AI auditing: The Broken Bus on the Road to AI Accountability. 612-643 - Augustin Godinot, Erwan Le Merrer, Gilles Trédan, Camilla Penzo, François Taïani:
Under manipulations, are some AI models harder to audit? 644-664 - Theodora Worledge, Judy Hanwen Shen, Nicole Meister, Caleb Winston, Carlos Guestrin:
Unifying Corroborative and Contributive Attributions in Large Language Models. 665-683 - Hossein Hajipour, Keno Hassler, Thorsten Holz, Lea Schönherr, Mario Fritz:
CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models. 684-709 - Nishtha Madaan, Srikanta Bedathur:
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion. 710-722 - Kamala Varma, Arda Numanoglu, Yigitcan Kaya, Tudor Dumitras:
Understanding, Uncovering, and Mitigating the Causes of Inference Slowdown for Language Models. 723-740
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