Vasisht Duddu

CoRR, January, 2025

Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations.

[BibT_eX]

[DOI]

Lachlan J. Gunn

Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy, 2025

Espresso: Robust Concept Filtering in Text-to-Image Models.

[BibT_eX]

[DOI]

Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy, 2025

2024

Combining Machine Learning Defenses without Conflicts.

[BibT_eX]

[DOI]

Rui Zhang

CoRR, 2024

LLM-CI: Assessing Contextual Integrity Norms in Language Models.

[BibT_eX]

[DOI]

Yan Shvartzshnaider

John Lacalamita

CoRR, 2024

Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations.

[BibT_eX]

[DOI]

CoRR, 2024

Espresso: Robust Concept Filtering in Text-to-Image Models.

[BibT_eX]

[DOI]

CoRR, 2024

On the Alignment of Group Fairness with Attribute Privacy.

[BibT_eX]

[DOI]

Jan Aalmoes

Proceedings of the Web Information Systems Engineering - WISE 2024, 2024

GrOVe: Ownership Verification of Graph Neural Networks using Embeddings.

[BibT_eX]

[DOI]

Asim Waheed

Proceedings of the IEEE Symposium on Security and Privacy, 2024

SoK: Unintended Interactions among Machine Learning Defenses and Risks.

[BibT_eX]

[DOI]

Sebastian Szyller

Proceedings of the IEEE Symposium on Security and Privacy, 2024

Attesting Distributional Properties of Training Data for Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Computer Security - ESORICS 2024, 2024

2023

Comprehension from Chaos: Towards Informed Consent for Private Computation.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022

Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks.

[BibT_eX]

[DOI]

Jan Aalmoes

CoRR, 2022

Comprehension from Chaos: What Users Understand and Expect from Private Computation.

[BibT_eX]

[DOI]

CoRR, 2022

Towards privacy aware deep learning for embedded systems.

[BibT_eX]

[DOI]

Virat Shejwalkar

Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

Inferring Sensitive Attributes from Model Explanations.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning.

[BibT_eX]

[DOI]

Sebastian Szyller

CoRR, 2021

Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Fault tolerance of neural networks in adversarial settings.

[BibT_eX]

[DOI]

N. Rajesh Pillai

J. Intell. Fuzzy Syst., 2020

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning.

[BibT_eX]

[DOI]

Virat Shejwalkar

CoRR, 2020

Towards Enhancing Fault Tolerance in Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the MobiQuitous '20: Computing, 2020

Quantifying Privacy Leakage in Graph Embedding.

[BibT_eX]

[DOI]

Virat Shejwalkar

Proceedings of the MobiQuitous '20: Computing, 2020

Quantifying (Hyper) Parameter Leakage in Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 6th IEEE International Conference on Multimedia Big Data, 2020

2019

Adversarial Fault Tolerant Training for Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Stealing Neural Networks via Timing Side Channels.

[BibT_eX]

[DOI]

Debasis Samanta

CoRR, 2018

Network and Security Analysis of Anonymous Communication Networks.

[BibT_eX]

[DOI]

Debasis Samanta

CoRR, 2018

Fuzzy Graph Modelling of Anonymous Networks.

[BibT_eX]

[DOI]

Debasis Samanta