Cikkek nyilvánosan hozzáférhető megbízással - Tianhao WangTovábbi információ
Sehol sem hozzáférhető: 1
Edge-Protected Triangle Count Estimation under Relationship Local Differential Privacy
Y Liu, T Wang, Y Liu, H Chen, C Li
IEEE Transactions on Knowledge and Data Engineering, 2024
Megbízások: National Natural Science Foundation of China
Valahol hozzáférhető: 37
Locally differentially private protocols for frequency estimation
T Wang, J Blocki, N Li, S Jha
Proceedings of the 26th USENIX Security Symposium, 2017
Megbízások: US National Science Foundation
Privacy at scale: Local differential privacy in practice
G Cormode, S Jha, T Kulkarni, N Li, D Srivastava, T Wang
Proceedings of the 2018 International Conference on Management of Data, 1655 …, 2018
Megbízások: US National Science Foundation
When machine unlearning jeopardizes privacy
M Chen, Z Zhang, T Wang, M Backes, M Humbert, Y Zhang
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021
Megbízások: US National Science Foundation, Helmholtz Association
Locally differentially private frequent itemset mining
T Wang, N Li, S Jha
2018 IEEE Symposium on Security and Privacy (SP), 127-143, 2018
Megbízások: US National Science Foundation
Graph unlearning
M Chen, Z Zhang, T Wang, M Backes, M Humbert, Y Zhang
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022
Megbízások: US National Science Foundation, Helmholtz Association
Calm: Consistent adaptive local marginal for marginal release under local differential privacy
Z Zhang, T Wang, N Li, S He, J Chen
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018
Megbízások: US National Science Foundation, National Natural Science Foundation of China
PrivSyn: Differentially Private Data Synthesis
Z Zhang, T Wang, N Li, J Honorio, M Backes, S He, J Chen, Y Zhang
USENIX Security, 2021
Megbízások: US National Science Foundation, National Natural Science Foundation of China …
An empirical analysis of memorization in fine-tuned autoregressive language models
F Mireshghallah, A Uniyal, T Wang, DK Evans, T Berg-Kirkpatrick
Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022
Megbízások: US National Science Foundation
Estimating numerical distributions under local differential privacy
Z Li, T Wang, M Lopuhaä-Zwakenberg, N Li, B Škoric
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
Megbízások: US National Science Foundation, Netherlands Organisation for Scientific Research
Locally Differentially Private Frequency Estimation with Consistency
T Wang, M Lopuhaä-Zwakenberg, Z Li, B Skoric, N Li
NDSS, 2020
Megbízások: US National Science Foundation, Netherlands Organisation for Scientific Research
Answering multi-dimensional analytical queries under local differential privacy
T Wang, B Ding, J Zhou, C Hong, Z Huang, N Li, S Jha
Proceedings of the 2019 International Conference on Management of Data, 159-176, 2019
Megbízások: US National Science Foundation, US Department of Defense
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
Megbízások: US National Science Foundation
Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension
A Xiong, T Wang, N Li, S Jha
2020 IEEE Symposium on Security and Privacy (SP), 392-410, 2020
Megbízások: US National Science Foundation
DP-Forward: Fine-tuning and inference on language models with differential privacy in forward pass
M Du, X Yue, SSM Chow, T Wang, C Huang, H Sun
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications …, 2023
Megbízások: US National Science Foundation, Research Grants Council, Hong Kong
PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Models
H Wang, Z Zhang, T Wang, S He, M Backes, J Chen, Y Zhang
32nd USENIX Security Symposium (USENIX Security 23), 1649-1666, 2023
Megbízások: US National Science Foundation, National Natural Science Foundation of China …
Locally differentially private sparse vector aggregation
M Zhou, T Wang, THH Chan, G Fanti, E Shi
2022 IEEE Symposium on Security and Privacy (SP), 422-439, 2022
Megbízások: US National Science Foundation, US Department of Defense, Research Grants …
Federated Boosted Decision Trees with Differential Privacy
S Maddock, G Cormode, T Wang, C Maple, S Jha
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022
Megbízások: US National Science Foundation, US Department of Defense, UK Engineering and …
Backdoor Attacks via Machine Unlearning
Z Liu, T Wang, M Huai, C Miao
Proceedings of the AAAI Conference on Artificial Intelligence 38 (13), 14115 …, 2024
Megbízások: US National Science Foundation
Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning?
R Wen, Z Zhao, Z Liu, M Backes, T Wang, Y Zhang
The Eleventh International Conference on Learning Representations, 2022
Megbízások: US National Science Foundation, Helmholtz Association
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