CRAM: Efficient Hardware-Based Memory Compression for Bandwidth Enhancement. - Vinson Young; Sanjay Kariyappa; Moinuddin K. Qureshi (CoRR, 2018)

Enabling Transparent Memory-Compression for Commodity Memory Systems. - Vinson Young; Sanjay Kariyappa; Moinuddin K. Qureshi (HPCA, 2019)

Improving Adversarial Robustness of Ensembles with Diversity Training. - Sanjay Kariyappa; Moinuddin K. Qureshi (CoRR, 2019)

Defending Against Model Stealing Attacks With Adaptive Misinformation. - Sanjay Kariyappa; Moinuddin K. Qureshi (CVPR, 2020)

MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation. - Sanjay Kariyappa; Atul Prakash 001; Moinuddin K. Qureshi (CVPR, 2021)

Protecting DNNs from Theft using an Ensemble of Diverse Models. - Sanjay Kariyappa; Atul Prakash 001; Moinuddin K. Qureshi (ICLR, 2021)

Bespoke Cache Enclaves: Fine-Grained and Scalable Isolation from Cache Side-Channels via Flexible Set-Partitioning. - Gururaj Saileshwar; Sanjay Kariyappa; Moinuddin K. Qureshi (SEED, 2021)

Enabling Inference Privacy with Adaptive Noise Injection. - Sanjay Kariyappa; Ousmane Dia; Moinuddin K. Qureshi (CoRR, 2021)

Gradient Inversion Attack: Leaking Private Labels in Two-Party Split Learning. - Sanjay Kariyappa; Moinuddin K. Qureshi (CoRR, 2021)

Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information. - Kiwan Maeng; Chuan Guo 001; Sanjay Kariyappa; G. Edward Suh (CoRR, 2022)

Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. - Sanjay Kariyappa; Chuan Guo 001; Kiwan Maeng; Wenjie Xiong 001; G. Edward Suh; Moinuddin K. Qureshi; Hsien-Hsin S. Lee (ICML, 2023)

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information. - Kiwan Maeng; Chuan Guo 001; Sanjay Kariyappa; G. Edward Suh (NeurIPS, 2023)

ExPLoit: Extracting Private Labels in Split Learning. - Sanjay Kariyappa; Moinuddin K. Qureshi (SaTML, 2023)

Privacy-Preserving Algorithmic Recourse. - Sikha Pentyala; Shubham Sharma; Sanjay Kariyappa; Freddy Lécué; Daniele Magazzeni (CoRR, 2023)

Understanding and Mitigating Privacy Vulnerabilities in Deep Learning. - Sanjay Kariyappa (2023)

SHAP@k: Efficient and Probably Approximately Correct (PAC) Identification of Top-K Features. - Sanjay Kariyappa; Leonidas Tsepenekas; Freddy Lécué; Daniele Magazzeni (AAAI, 2024)

Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions. - Sanjay Kariyappa; Freddy Lécué; Saumitra Mishra; Christopher Pond; Daniele Magazzeni; Manuela Veloso (ICML, 2024)

Information Flow Control in Machine Learning through Modular Model Architecture. - Trishita Tiwari; Suchin Gururangan; Chuan Guo 001; Weizhe Hua; Sanjay Kariyappa; Udit Gupta; Wenjie Xiong 001; Kiwan Maeng; Hsien-Hsin S. Lee; G. Edward Suh (USENIX Security Symposium, 2024)

Interpretable LLM-based Table Question Answering. - Giang Nguyen 004; Ivan Brugere; Shubham Sharma; Sanjay Kariyappa; Anh Totti Nguyen; Freddy Lécué (Trans. Mach. Learn. Res., 2025)

Stronger Enforcement of Instruction Hierarchy via Augmented Intermediate Representations. - Sanjay Kariyappa; G. Edward Suh (CoRR, 2025)