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Matt Fredrikson
Matthew Fredrikson
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Journal Articles
- 2022
- [j7]Helena Montenegro, Wilson Silva, Alex Gaudio, Matt Fredrikson, Asim Smailagic, Jaime S. Cardoso:
Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy. IEEE Access 10: 28333-28347 (2022) - [j6]Daniel Gibert, Matt Fredrikson, Carles Mateu, Jordi Planes, Quan Le:
Enhancing the insertion of NOP instructions to obfuscate malware via deep reinforcement learning. Comput. Secur. 113: 102543 (2022) - [j5]Klas Leino, Chi Zhang, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Degradation Attacks on Certifiably Robust Neural Networks. Trans. Mach. Learn. Res. 2022 (2022) - 2020
- [j4]Samuel Yeom, Irene Giacomelli, Alan Menaged, Matt Fredrikson, Somesh Jha:
Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning. J. Comput. Secur. 28(1): 35-70 (2020) - 2018
- [j3]Haojian Jin, Minyi Liu, Kevan Dodhia, Yuanchun Li, Gaurav Srivastava, Matthew Fredrikson, Yuvraj Agarwal, Jason I. Hong:
Why Are They Collecting My Data?: Inferring the Purposes of Network Traffic in Mobile Apps. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(4): 173:1-173:27 (2018) - 2017
- [j2]Yuanchun Li, Fanglin Chen, Toby Jia-Jun Li, Yao Guo, Gang Huang, Matthew Fredrikson, Yuvraj Agarwal, Jason I. Hong:
PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3): 76:1-76:26 (2017) - 2009
- [j1]Chen Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, Jiawei Han:
Mining Graph Patterns Efficiently via Randomized Summaries. Proc. VLDB Endow. 2(1): 742-753 (2009)
Conference and Workshop Papers
- 2024
- [c51]Kai Hu, Klas Leino, Zifan Wang, Matt Fredrikson:
A Recipe for Improved Certifiable Robustness. ICLR 2024 - 2023
- [c50]Ravi Mangal, Zifan Wang, Chi Zhang, Klas Leino, Corina S. Pasareanu, Matt Fredrikson:
On the Perils of Cascading Robust Classifiers. ICLR 2023 - [c49]Kai Hu, Andy Zou, Zifan Wang, Klas Leino, Matt Fredrikson:
Unlocking Deterministic Robustness Certification on ImageNet. NeurIPS 2023 - [c48]Zifan Wang, Saranya Vijayakumar, Kaiji Lu, Vijay Ganesh, Somesh Jha, Matt Fredrikson:
Grounding Neural Inference with Satisfiability Modulo Theories. NeurIPS 2023 - 2022
- [c47]Klas Leino, Aymeric Fromherz, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Self-correcting Neural Networks for Safe Classification. NSV/FoMLAS@CAV 2022: 96-130 - [c46]Emily Black, Klas Leino, Matt Fredrikson:
Selective Ensembles for Consistent Predictions. ICLR 2022 - [c45]Emily Black, Zifan Wang, Matt Fredrikson:
Consistent Counterfactuals for Deep Models. ICLR 2022 - [c44]Zifan Wang, Matt Fredrikson, Anupam Datta:
Robust Models Are More Interpretable Because Attributions Look Normal. ICML 2022: 22625-22651 - [c43]Han Zhang, Yuvraj Agarwal, Matt Fredrikson:
TEO: ephemeral ownership for IoT devices to provide granular data control. MobiSys 2022: 302-315 - [c42]Han Zhang, Yuvraj Agarwal, Matt Fredrikson:
Protecting user data through ephemeral ownership of IoT devices. MobiSys 2022: 620-621 - 2021
- [c41]Abhishek Bichhawat, Matt Fredrikson, Jean Yang:
Automating Audit with Policy Inference. CSF 2021: 1-16 - [c40]Emily Black, Matt Fredrikson:
Leave-one-out Unfairness. FAccT 2021: 285-295 - [c39]Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Fast Geometric Projections for Local Robustness Certification. ICLR 2021 - [c38]Klas Leino, Zifan Wang, Matt Fredrikson:
Globally-Robust Neural Networks. ICML 2021: 6212-6222 - [c37]Anupam Datta, Matt Fredrikson, Klas Leino, Kaiji Lu, Shayak Sen, Zifan Wang:
Machine Learning Explainability and Robustness: Connected at the Hip. KDD 2021: 4035-4036 - [c36]Anupam Datta, Matt Fredrikson, Klas Leino, Kaiji Lu, Shayak Sen, Ricardo Shih, Zifan Wang:
Exploring Conceptual Soundness with TruLens. NeurIPS (Competition and Demos) 2021: 302-307 - [c35]Klas Leino, Matt Fredrikson:
Relaxing Local Robustness. NeurIPS 2021: 17072-17083 - [c34]Han Zhang, Abhijith Anilkumar, Matt Fredrikson, Yuvraj Agarwal:
Capture: Centralized Library Management for Heterogeneous IoT Devices. USENIX Security Symposium 2021: 4187-4204 - [c33]Han Zhang, Chi Zhang, Arthur Azevedo de Amorim, Yuvraj Agarwal, Matt Fredrikson, Limin Jia:
Netter: Probabilistic, Stateful Network Models. VMCAI 2021: 486-508 - 2020
- [c32]Kaiji Lu, Piotr Mardziel, Klas Leino, Matt Fredrikson, Anupam Datta:
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models. ACL 2020: 4748-4757 - [c31]Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. AISTATS 2020: 155-166 - [c30]Abhishek Bichhawat, Matt Fredrikson, Jean Yang, Akash Trehan:
Contextual and Granular Policy Enforcement in Database-backed Applications. AsiaCCS 2020: 432-444 - [c29]Zifan Wang, Piotr Mardziel, Anupam Datta, Matt Fredrikson:
Interpreting Interpretations: Organizing Attribution Methods by Criteria. CVPR Workshops 2020: 48-55 - [c28]Emily Black, Samuel Yeom, Matt Fredrikson:
FlipTest: fairness testing via optimal transport. FAT* 2020: 111-121 - [c27]Samuel Yeom, Matt Fredrikson:
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness. IJCAI 2020: 437-443 - [c26]Arthur Azevedo de Amorim, Matt Fredrikson, Limin Jia:
Reconciling noninterference and gradual typing. LICS 2020: 116-129 - [c25]Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, Anupam Datta:
Smoothed Geometry for Robust Attribution. NeurIPS 2020 - [c24]Klas Leino, Matt Fredrikson:
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference. USENIX Security Symposium 2020: 1605-1622 - 2019
- [c23]Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, Anupam Datta:
Feature-Wise Bias Amplification. ICLR (Poster) 2019 - 2018
- [c22]Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha:
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. CSF 2018: 268-282 - [c21]Ryan Wagner, David Garlan, Matt Fredrikson:
Quantitative underpinnings of secure, graceful degradation: poster. HotSoS 2018: 25:1 - [c20]Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, Linyi Li:
Influence-Directed Explanations for Deep Convolutional Networks. ITC 2018: 1-8 - [c19]Samuel Yeom, Anupam Datta, Matt Fredrikson:
Hunting for Discriminatory Proxies in Linear Regression Models. NeurIPS 2018: 4573-4583 - 2017
- [c18]Anupam Datta, Matthew Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen:
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs. CCS 2017: 1193-1210 - [c17]Van Chan Ngo, Mario Dehesa-Azuara, Matthew Fredrikson, Jan Hoffmann:
Verifying and Synthesizing Constant-Resource Implementations with Types. IEEE Symposium on Security and Privacy 2017: 710-728 - 2016
- [c16]Xi Wu, Matthew Fredrikson, Somesh Jha, Jeffrey F. Naughton:
A Methodology for Formalizing Model-Inversion Attacks. CSF 2016: 355-370 - [c15]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. EuroS&P 2016: 372-387 - 2015
- [c14]Matt Fredrikson, Somesh Jha, Thomas Ristenpart:
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. CCS 2015: 1322-1333 - 2014
- [c13]Drew Davidson, Matt Fredrikson, Benjamin Livshits:
MoRePriv: mobile OS support for application personalization and privacy. ACSAC 2014: 236-245 - [c12]Matthew Fredrikson, Somesh Jha:
Satisfiability modulo counting: a new approach for analyzing privacy properties. CSL-LICS 2014: 42:1-42:10 - [c11]Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon M. Lin, David Page, Thomas Ristenpart:
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. USENIX Security Symposium 2014: 17-32 - [c10]Stephen Checkoway, Ruben Niederhagen, Adam Everspaugh, Matthew Green, Tanja Lange, Thomas Ristenpart, Daniel J. Bernstein, Jake Maskiewicz, Hovav Shacham, Matthew Fredrikson:
On the Practical Exploitability of Dual EC in TLS Implementations. USENIX Security Symposium 2014: 319-335 - [c9]Matthew Fredrikson, Benjamin Livshits:
ZØ: An Optimizing Distributing Zero-Knowledge Compiler. USENIX Security Symposium 2014: 909-924 - 2013
- [c8]Somesh Jha, Matthew Fredrikson, Mihai Christodorescu, Reiner Sailer, Xifeng Yan:
Synthesizing near-optimal malware specifications from suspicious behaviors. MALWARE 2013: 41-50 - 2012
- [c7]Matthew Fredrikson, Richard Joiner, Somesh Jha, Thomas W. Reps, Phillip A. Porras, Hassen Saïdi, Vinod Yegneswaran:
Efficient Runtime Policy Enforcement Using Counterexample-Guided Abstraction Refinement. CAV 2012: 548-563 - 2011
- [c6]Matthew Fredrikson, Mihai Christodorescu, Somesh Jha:
Dynamic Behavior Matching: A Complexity Analysis and New Approximation Algorithms. CADE 2011: 252-267 - [c5]Arjun Guha, Matthew Fredrikson, Benjamin Livshits, Nikhil Swamy:
Verified Security for Browser Extensions. IEEE Symposium on Security and Privacy 2011: 115-130 - [c4]Matthew Fredrikson, Benjamin Livshits:
RePriv: Re-imagining Content Personalization and In-browser Privacy. IEEE Symposium on Security and Privacy 2011: 131-146 - 2010
- [c3]Matt Fredrikson, Somesh Jha, Mihai Christodorescu, Reiner Sailer, Xifeng Yan:
Synthesizing Near-Optimal Malware Specifications from Suspicious Behaviors. IEEE Symposium on Security and Privacy 2010: 45-60 - [c2]Roberto Paleari, Lorenzo Martignoni, Emanuele Passerini, Drew Davidson, Matt Fredrikson, Jonathon T. Giffin, Somesh Jha:
Automatic Generation of Remediation Procedures for Malware Infections. USENIX Security Symposium 2010: 419-434 - 2008
- [c1]Lorenzo Martignoni, Elizabeth Stinson, Matt Fredrikson, Somesh Jha, John C. Mitchell:
A Layered Architecture for Detecting Malicious Behaviors. RAID 2008: 78-97
Parts in Books or Collections
- 2011
- [p4]Mihai Christodorescu, Matthew Fredrikson, Somesh Jha, Jonathon T. Giffin:
End-to-End Software Diversification of Internet Services. Moving Target Defense 2011: 117-130 - 2010
- [p3]Chen Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, Jiawei Han:
Mining Large Information Networks by Graph Summarization. Link Mining 2010: 475-501 - [p2]Paul Barford, Marc Dacier, Thomas G. Dietterich, Matt Fredrikson, Jonathon T. Giffin, Sushil Jajodia, Somesh Jha, Jason H. Li, Peng Liu, Peng Ning, Xinming Ou, Dawn Song, Laura Strater, Vipin Swarup, George P. Tadda, C. Wang, John Yen:
Cyber SA: Situational Awareness for Cyber Defense. Cyber Situational Awareness 2010: 3-13 - [p1]Matt Fredrikson, Mihai Christodorescu, Jonathon T. Giffin, Somesh Jha:
A Declarative Framework for Intrusion Analysis. Cyber Situational Awareness 2010: 179-200
Informal and Other Publications
- 2024
- [i46]Kai Hu, Weichen Yu, Tianjun Yao, Xiang Li, Wenhe Liu, Lijun Yu, Yining Li, Kai Chen, Zhiqiang Shen, Matt Fredrikson:
Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization. CoRR abs/2405.09113 (2024) - [i45]Han Zhang, Zifan Wang, Mihir Dhamankar, Matt Fredrikson, Yuvraj Agarwal:
VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices. CoRR abs/2406.00586 (2024) - [i44]Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, Dan Hendrycks:
Improving Alignment and Robustness with Circuit Breakers. CoRR abs/2406.04313 (2024) - [i43]Weiran Lin, Anna Gerchanovsky, Omer Akgul, Lujo Bauer, Matt Fredrikson, Zifan Wang:
Sales Whisperer: A Human-Inconspicuous Attack on LLM Brand Recommendations. CoRR abs/2406.04755 (2024) - [i42]Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, Eric Winsor, Jerome Wynne, Yarin Gal, Xander Davies:
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. CoRR abs/2410.09024 (2024) - [i41]Priyanshu Kumar, Elaine Lau, Saranya Vijayakumar, Tu Trinh, Scale Red Team, Elaine Chang, Vaughn Robinson, Sean Hendryx, Shuyan Zhou, Matt Fredrikson, Summer Yue, Zifan Wang:
Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents. CoRR abs/2410.13886 (2024) - 2023
- [i40]Matt Fredrikson, Kaiji Lu, Saranya Vijayakumar, Somesh Jha, Vijay Ganesh, Zifan Wang:
Learning Modulo Theories. CoRR abs/2301.11435 (2023) - [i39]Kai Hu, Andy Zou, Zifan Wang, Klas Leino, Matt Fredrikson:
Scaling in Depth: Unlocking Robustness Certification on ImageNet. CoRR abs/2301.12549 (2023) - [i38]Andy Zou, Zifan Wang, J. Zico Kolter, Matt Fredrikson:
Universal and Transferable Adversarial Attacks on Aligned Language Models. CoRR abs/2307.15043 (2023) - [i37]Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, Dan Hendrycks:
Representation Engineering: A Top-Down Approach to AI Transparency. CoRR abs/2310.01405 (2023) - [i36]Kai Hu, Klas Leino, Zifan Wang, Matt Fredrikson:
A Recipe for Improved Certifiable Robustness: Capacity and Data. CoRR abs/2310.02513 (2023) - [i35]Ravi Mangal, Klas Leino, Zifan Wang, Kai Hu, Weicheng Yu, Corina S. Pasareanu, Anupam Datta, Matt Fredrikson:
Is Certifying 𝓁p Robustness Still Worthwhile? CoRR abs/2310.09361 (2023) - [i34]Chi Zhang, Zifan Wang, Ravi Mangal, Matt Fredrikson, Limin Jia, Corina S. Pasareanu:
Transfer Attacks and Defenses for Large Language Models on Coding Tasks. CoRR abs/2311.13445 (2023) - 2022
- [i33]Zifan Wang, Yuhang Yao, Chaoran Zhang, Han Zhang, Youjie Kang, Carlee Joe-Wong, Matt Fredrikson, Anupam Datta:
Faithful Explanations for Deep Graph Models. CoRR abs/2205.11850 (2022) - [i32]Ravi Mangal, Zifan Wang, Chi Zhang, Klas Leino, Corina S. Pasareanu, Matt Fredrikson:
On the Perils of Cascading Robust Classifiers. CoRR abs/2206.00278 (2022) - [i31]Marc Juarez, Samuel Yeom, Matt Fredrikson:
Black-Box Audits for Group Distribution Shifts. CoRR abs/2209.03620 (2022) - 2021
- [i30]Klas Leino, Zifan Wang, Matt Fredrikson:
Globally-Robust Neural Networks. CoRR abs/2102.08452 (2021) - [i29]Zifan Wang, Matt Fredrikson, Anupam Datta:
Boundary Attributions Provide Normal (Vector) Explanations. CoRR abs/2103.11257 (2021) - [i28]Jason I. Hong, Yuvraj Agarwal, Matt Fredrikson, Mike Czapik, Shawn Hanna, Swarup Sahoo, Judy Chun, Won-Woo Chung, Aniruddh Iyer, Ally Liu, Shen Lu, Rituparna Roychoudhury, Qian Wang, Shan Wang, Siqi Wang, Vida Zhang, Jessica Zhao, Yuan Jiang, Haojian Jin, Sam Kim, Evelyn Kuo, Tianshi Li, Jinping Liu, Yile Liu, Robert Zhang:
The Design of the User Interfaces for Privacy Enhancements for Android. CoRR abs/2104.12032 (2021) - [i27]Klas Leino, Matt Fredrikson:
Relaxing Local Robustness. CoRR abs/2106.06624 (2021) - [i26]Emily Black, Matt Fredrikson:
Leave-one-out Unfairness. CoRR abs/2107.10171 (2021) - [i25]Klas Leino, Aymeric Fromherz, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Self-Repairing Neural Networks: Provable Safety for Deep Networks via Dynamic Repair. CoRR abs/2107.11445 (2021) - [i24]Emily Black, Zifan Wang, Matt Fredrikson, Anupam Datta:
Consistent Counterfactuals for Deep Models. CoRR abs/2110.03109 (2021) - [i23]Emily Black, Klas Leino, Matt Fredrikson:
Selective Ensembles for Consistent Predictions. CoRR abs/2111.08230 (2021) - [i22]Daniel Gibert, Matt Fredrikson, Carles Mateu, Jordi Planes, Quan Le:
Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning. CoRR abs/2111.09626 (2021) - 2020
- [i21]Aymeric Fromherz, Klas Leino, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Fast Geometric Projections for Local Robustness Certification. CoRR abs/2002.04742 (2020) - [i20]Samuel Yeom, Matt Fredrikson:
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness. CoRR abs/2002.07738 (2020) - [i19]Zifan Wang, Piotr Mardziel, Anupam Datta, Matt Fredrikson:
Interpreting Interpretations: Organizing Attribution Methods by Criteria. CoRR abs/2002.07985 (2020) - [i18]Kaiji Lu, Piotr Mardziel, Klas Leino, Matt Fredrikson, Anupam Datta:
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models. CoRR abs/2005.01190 (2020) - [i17]Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, Anupam Datta:
Smoothed Geometry for Robust Attribution. CoRR abs/2006.06643 (2020) - 2019
- [i16]Emily Black, Samuel Yeom, Matt Fredrikson:
FlipTest: Fairness Auditing via Optimal Transport. CoRR abs/1906.09218 (2019) - [i15]Klas Leino, Matt Fredrikson:
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference. CoRR abs/1906.11798 (2019) - [i14]Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. CoRR abs/1906.11813 (2019) - 2018
- [i13]Van Chan Ngo, Mario Dehesa-Azuara, Matthew Fredrikson, Jan Hoffmann:
Verifying and Synthesizing Constant-Resource Implementations with Types. CoRR abs/1801.01896 (2018) - [i12]Klas Leino, Linyi Li, Shayak Sen, Anupam Datta, Matt Fredrikson:
Influence-Directed Explanations for Deep Convolutional Networks. CoRR abs/1802.03788 (2018) - [i11]Shayak Sen, Piotr Mardziel, Anupam Datta, Matthew Fredrikson:
Supervising Feature Influence. CoRR abs/1803.10815 (2018) - [i10]Samuel Yeom, Anupam Datta, Matt Fredrikson:
Hunting for Discriminatory Proxies in Linear Regression Models. CoRR abs/1810.07155 (2018) - [i9]Abhishek Bichhawat, Akash Trehan, Jean Yang, Matt Fredrikson:
ESTRELA: Automated Policy Enforcement Across Remote APIs. CoRR abs/1811.08234 (2018) - [i8]Klas Leino, Matt Fredrikson, Emily Black, Shayak Sen, Anupam Datta:
Feature-Wise Bias Amplification. CoRR abs/1812.08999 (2018) - 2017
- [i7]Anupam Datta, Matthew Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen:
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs. CoRR abs/1705.07807 (2017) - [i6]Anupam Datta, Matt Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen:
Proxy Non-Discrimination in Data-Driven Systems. CoRR abs/1707.08120 (2017) - [i5]Gaurav Srivastava, Saksham Chitkara, Kevin Ku, Swarup Kumar Sahoo, Matt Fredrikson, Jason I. Hong, Yuvraj Agarwal:
PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information Leakage. CoRR abs/1708.06384 (2017) - [i4]Samuel Yeom, Matt Fredrikson, Somesh Jha:
The Unintended Consequences of Overfitting: Training Data Inference Attacks. CoRR abs/1709.01604 (2017) - 2015
- [i3]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. CoRR abs/1511.07528 (2015) - [i2]Xi Wu, Matthew Fredrikson, Wentao Wu, Somesh Jha, Jeffrey F. Naughton:
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences. CoRR abs/1512.06388 (2015) - [i1]Bruce Schneier, Matthew Fredrikson, Tadayoshi Kohno, Thomas Ristenpart:
Surreptitiously Weakening Cryptographic Systems. IACR Cryptol. ePrint Arch. 2015: 97 (2015)