Matt Fredrikson

Proceedings of the MobiSys '22: The 20th Annual International Conference on Mobile Systems, Applications and Services, Portland, Oregon, 27 June 2022, 2022

Robust Models Are More Interpretable Because Attributions Look Normal.

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[DOI]

Anupam Datta

Proceedings of the International Conference on Machine Learning, 2022

Consistent Counterfactuals for Deep Models.

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[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Selective Ensembles for Consistent Predictions.

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[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Self-correcting Neural Networks for Safe Classification.

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[DOI]

Proceedings of the Software Verification and Formal Methods for ML-Enabled Autonomous Systems, 2022

2021

Consistent Counterfactuals for Deep Models.

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[DOI]

CoRR, 2021

Self-Repairing Neural Networks: Provable Safety for Deep Networks via Dynamic Repair.

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[DOI]

CoRR, 2021

The Design of the User Interfaces for Privacy Enhancements for Android.

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[DOI]

Rituparna Roychoudhury

CoRR, 2021

Boundary Attributions Provide Normal (Vector) Explanations.

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[DOI]

Anupam Datta

CoRR, 2021

Netter: Probabilistic, Stateful Network Models.

[BibT_eX]

[DOI]

Han Zhang

Chi Zhang

Arthur Azevedo de Amorim

Yuvraj Agarwal

Limin Jia

Proceedings of the Verification, Model Checking, and Abstract Interpretation, 2021

Capture: Centralized Library Management for Heterogeneous IoT Devices.

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[DOI]

Proceedings of the 30th USENIX Security Symposium, 2021

Relaxing Local Robustness.

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[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Exploring Conceptual Soundness with TruLens.

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[DOI]

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Machine Learning Explainability and Robustness: Connected at the Hip.

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[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Globally-Robust Neural Networks.

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[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Fast Geometric Projections for Local Robustness Certification.

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[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Leave-one-out Unfairness.

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[DOI]

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Automating Audit with Policy Inference.

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[DOI]

Abhishek Bichhawat

Jean Yang

Proceedings of the 34th IEEE Computer Security Foundations Symposium, 2021

2020

Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning.

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[DOI]

J. Comput. Secur., 2020

Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference.

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[DOI]

Proceedings of the 29th USENIX Security Symposium, 2020

Smoothed Geometry for Robust Attribution.

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[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Reconciling noninterference and gradual typing.

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[DOI]

Arthur Azevedo de Amorim

Limin Jia

Proceedings of the LICS '20: 35th Annual ACM/IEEE Symposium on Logic in Computer Science, 2020

Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness.

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[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

FlipTest: fairness testing via optimal transport.

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[DOI]

Proceedings of the FAT* '20: Conference on Fairness, 2020

Interpreting Interpretations: Organizing Attribution Methods by Criteria.

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[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Contextual and Granular Policy Enforcement in Database-backed Applications.

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[DOI]

Proceedings of the ASIA CCS '20: The 15th ACM Asia Conference on Computer and Communications Security, 2020

Learning Fair Representations for Kernel Models.

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[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models.

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[DOI]

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

FlipTest: Fairness Auditing via Optimal Transport.

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[DOI]

CoRR, 2019

Feature-Wise Bias Amplification.

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[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Why Are They Collecting My Data?: Inferring the Purposes of Network Traffic in Mobile Apps.

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[DOI]

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2018

ESTRELA: Automated Policy Enforcement Across Remote APIs.

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[DOI]

CoRR, 2018

Supervising Feature Influence.

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[DOI]

CoRR, 2018

Hunting for Discriminatory Proxies in Linear Regression Models.

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[DOI]

Anupam Datta

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Influence-Directed Explanations for Deep Convolutional Networks.

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[DOI]

Proceedings of the IEEE International Test Conference, 2018

Quantitative underpinnings of secure, graceful degradation: poster.

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[DOI]

Ryan Wagner

David Garlan

Proceedings of the 5th Annual Symposium and Bootcamp on Hot Topics in the Science of Security, 2018

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting.

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[DOI]

Proceedings of the 31st IEEE Computer Security Foundations Symposium, 2018

2017

PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps.

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[DOI]

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017

The Unintended Consequences of Overfitting: Training Data Inference Attacks.

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[DOI]

Somesh Jha

CoRR, 2017

PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information Leakage.

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[DOI]

CoRR, 2017

Proxy Non-Discrimination in Data-Driven Systems.

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[DOI]

CoRR, 2017

Verifying and Synthesizing Constant-Resource Implementations with Types.

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[DOI]

Proceedings of the 2017 IEEE Symposium on Security and Privacy, 2017

Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs.

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[DOI]

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017

2016

The Limitations of Deep Learning in Adversarial Settings.

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[DOI]

Proceedings of the IEEE European Symposium on Security and Privacy, 2016

A Methodology for Formalizing Model-Inversion Attacks.

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[DOI]

Proceedings of the IEEE 29th Computer Security Foundations Symposium, 2016

2015

Surreptitiously Weakening Cryptographic Systems.

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[DOI]

IACR Cryptol. ePrint Arch., 2015

Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences.

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[DOI]

CoRR, 2015

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures.

[BibT_eX]

[DOI]

Somesh Jha

Thomas Ristenpart

Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015

2014

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.

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[DOI]

Proceedings of the 23rd USENIX Security Symposium, San Diego, CA, USA, August 20-22, 2014., 2014

ZØ: An Optimizing Distributing Zero-Knowledge Compiler.

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[DOI]

Matthew Fredrikson

Benjamin Livshits

Proceedings of the 23rd USENIX Security Symposium, San Diego, CA, USA, August 20-22, 2014., 2014

On the Practical Exploitability of Dual EC in TLS Implementations.

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[DOI]

Proceedings of the 23rd USENIX Security Symposium, San Diego, CA, USA, August 20-22, 2014., 2014

Satisfiability modulo counting: a new approach for analyzing privacy properties.

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[DOI]

Matthew Fredrikson

Somesh Jha

Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Ninth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2014

MoRePriv: mobile OS support for application personalization and privacy.

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[DOI]

Drew Davidson