Fuzzy Graph Modelling of Anonymous Networks. - Vasisht Duddu; Debasis Samanta; D. Vijay Rao (SOFA (2), 2018)

Network and Security Analysis of Anonymous Communication Networks. - Vasisht Duddu; Debasis Samanta (CoRR, 2018)

Stealing Neural Networks via Timing Side Channels. - Vasisht Duddu; Debasis Samanta; D. Vijay Rao; Valentina Emilia Balas (CoRR, 2018)

Adversarial Fault Tolerant Training for Deep Neural Networks. - Vasisht Duddu; D. Vijay Rao; Valentina Emilia Balas (CoRR, 2019)

Quantifying (Hyper) Parameter Leakage in Machine Learning. - Vasisht Duddu; D. Vijay Rao (BigMM, 2020)

Quantifying Privacy Leakage in Graph Embedding. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (MobiQuitous, 2020)

Towards Enhancing Fault Tolerance in Neural Networks. - Vasisht Duddu; D. Vijay Rao; Valentina Emilia Balas (MobiQuitous, 2020)

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (CoRR, 2020)

Fault tolerance of neural networks in adversarial settings. - Vasisht Duddu; N. Rajesh Pillai; D. Vijay Rao; Valentina Emilia Balas (J. Intell. Fuzzy Syst., 2020)

Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks. - Sebastian Szyller; Vasisht Duddu; Tommi Gröndahl; N. Asokan (CoRR, 2021)

SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning. - Vasisht Duddu; Sebastian Szyller; N. Asokan (CoRR, 2021)

Inferring Sensitive Attributes from Model Explanations. - Vasisht Duddu; Antoine Boutet (CIKM, 2022)

Towards privacy aware deep learning for embedded systems. - Vasisht Duddu; Antoine Boutet; Virat Shejwalkar (SAC, 2022)

Comprehension from Chaos: What Users Understand and Expect from Private Computation. - Bailey Kacsmar; Vasisht Duddu; Kyle Tilbury; Blase Ur; Florian Kerschbaum (CoRR, 2022)

Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks. - Jan Aalmoes; Vasisht Duddu; Antoine Boutet (CoRR, 2022)

Comprehension from Chaos: Towards Informed Consent for Private Computation. - Bailey Kacsmar; Vasisht Duddu; Kyle Tilbury; Blase Ur; Florian Kerschbaum (CCS, 2023)

Attesting Distributional Properties of Training Data for Machine Learning. - Vasisht Duddu; Anudeep Das; Nora Khayata; Hossein Yalame; Thomas Schneider 003; N. Asokan (ESORICS (1), 2024)

SoK: Unintended Interactions among Machine Learning Defenses and Risks. - Vasisht Duddu; Sebastian Szyller; N. Asokan (SP, 2024)

GrOVe: Ownership Verification of Graph Neural Networks using Embeddings. - Asim Waheed; Vasisht Duddu; N. Asokan (SP, 2024)

On the Alignment of Group Fairness with Attribute Privacy. - Jan Aalmoes; Vasisht Duddu; Antoine Boutet (WISE (2), 2024)

Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations. - Vasisht Duddu; Oskari Järvinen; Lachlan J. Gunn; N. Asokan (CoRR, 2024)

LLM-CI: Assessing Contextual Integrity Norms in Language Models. - Yan Shvartzshnaider; Vasisht Duddu; John Lacalamita (CoRR, 2024)

Espresso: Robust Concept Filtering in Text-to-Image Models. - Anudeep Das; Vasisht Duddu; Rui Zhang 118; N. Asokan (CODASPY, 2025)

Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations. - Vasisht Duddu; Lachlan J. Gunn; N. Asokan (CODASPY, 2025)

Combining Machine Learning Defenses without Conflicts. - Vasisht Duddu; Rui Zhang 118; N. Asokan (Trans. Mach. Learn. Res., 2025)

Position: Contextual Integrity Washing for Language Models. - Yan Shvartzshnaider; Vasisht Duddu (CoRR, 2025)