Evaluating the robustness of neural networks: An extreme value theory approach TW Weng, H Zhang, PY Chen, J Yi, D Su, Y Gao, CJ Hsieh, L Daniel arXiv preprint arXiv:1801.10578, 2018 | 498 | 2018 |
Is robustness the cost of accuracy?--a comprehensive study on the robustness of 18 deep image classification models D Su, H Zhang, H Chen, J Yi, PY Chen, Y Gao Proceedings of the European conference on computer vision (ECCV), 631-648, 2018 | 419 | 2018 |
On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy N Li, W Qardaji, D Su Proceedings of the 7th ACM Symposium on Information, Computer and …, 2012 | 337 | 2012 |
Privbasis: Frequent itemset mining with differential privacy N Li, W Qardaji, D Su, J Cao arXiv preprint arXiv:1208.0093, 2012 | 218 | 2012 |
Membership privacy: A unifying framework for privacy definitions N Li, W Qardaji, D Su, Y Wu, W Yang Proceedings of the 2013 ACM SIGSAC conference on Computer & communications …, 2013 | 192 | 2013 |
Differentially private k-means clustering D Su, J Cao, N Li, E Bertino, H Jin Proceedings of the sixth ACM conference on data and application security and …, 2016 | 182 | 2016 |
Differential privacy: From theory to practice N Li, M Lyu, D Su, W Yang Morgan & Claypool, 2017 | 171 | 2017 |
Provably private data anonymization: Or, k-anonymity meets differential privacy N Li, WH Qardaji, D Su CoRR, abs/1101.2604 49, 55, 2011 | 156 | 2011 |
Understanding the sparse vector technique for differential privacy M Lyu, D Su, N Li arXiv preprint arXiv:1603.01699, 2016 | 155 | 2016 |
Defending against neural network model stealing attacks using deceptive perturbations T Lee, B Edwards, I Molloy, D Su 2019 IEEE Security and Privacy Workshops (SPW), 43-49, 2019 | 85 | 2019 |
Securing input data of deep learning inference systems via partitioned enclave execution Z Gu, H Huang, J Zhang, D Su, A Lamba, D Pendarakis, I Molloy arXiv preprint arXiv:1807.00969, 1-14, 2018 | 74* | 2018 |
Protection of confidentiality, privacy and ownership assurance in a blockchain based decentralized identity management system S Chari, H Gunasinghe, HM Krawczyk, A Kundu, KK Singh, D Su US Patent 10,833,861, 2020 | 70 | 2020 |
Differentially private k-means clustering and a hybrid approach to private optimization D Su, J Cao, N Li, E Bertino, M Lyu, H Jin ACM Transactions on Privacy and Security (TOPS) 20 (4), 1-33, 2017 | 55 | 2017 |
Continuous release of data streams under both centralized and local differential privacy T Wang, JQ Chen, Z Zhang, D Su, Y Cheng, Z Li, N Li, S Jha Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 49 | 2021 |
Deep neural network hardening framework BJ Edwards, T Lee, IM Molloy, D Su US Patent 11,443,178, 2022 | 48 | 2022 |
Defending against model stealing attacks using deceptive perturbations T Lee, B Edwards, I Molloy, D Su arXiv preprint arXiv:1806.00054, 2018 | 40 | 2018 |
Decentralized database identity management system KK Singh, SN Chari, A Kundu, S Muppidi, D Su US Patent 11,178,151, 2021 | 38 | 2021 |
Protection of confidentiality, privacy and financial fairness in a blockchain based decentralized identity management system S Chari, H Gunasinghe, A Kundu, KK Singh, D Su US Patent 10,715,317, 2020 | 34 | 2020 |
Defending against machine learning model stealing attacks using deceptive perturbations T Lee, B Edwards, I Molloy, D Su arXiv preprint arXiv:1806.00054, 2018 | 32 | 2018 |
Protecting cognitive systems from model stealing attacks T Lee, IM Molloy, D Su US Patent 11,023,593, 2021 | 30 | 2021 |