"differential privacy" Papers
96 papers found • Page 1 of 2
Conference
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Daogao Liu, Kunal Talwar
A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances
Zongrui Zou, Chenglin Fan, Michael Dinitz et al.
A New Federated Learning Framework Against Gradient Inversion Attacks
Pengxin Guo, Shuang Zeng, Wenhao Chen et al.
An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Johanna Düngler, Amartya Sanyal
A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
Bar Mahpud, Or Sheffet
Controlling The Spread of Epidemics on Networks with Differential Privacy
Dũng Nguyen, Aravind Srinivasan, Renata Valieva et al.
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang et al.
Deep Learning with Plausible Deniability
Wenxuan Bao, Shan Jin, Hadi Abdullah et al.
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Andrew Lowy, Daogao Liu
Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
Ming Wen, Jiaqi Zhu, Yuedong Xu et al.
Differentially Private Gomory-Hu Trees
Anders Aamand, Justin Chen, Mina Dalirrooyfard et al.
Differentially private learners for heterogeneous treatment effects
Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel
Differentially Private Prototypes for Imbalanced Transfer Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic et al.
Differentially Private Relational Learning with Entity-level Privacy Guarantees
Yinan Huang, Haoteng Yin, Eli Chien et al.
Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming
Zhao Song, Jianfei Xue, Lichen Zhang
Diffusion Federated Dataset
SEOKJU HAHN, Junghye Lee
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
Xinwei Zhang, Zhiqi Bu, Borja Balle et al.
Does Training with Synthetic Data Truly Protect Privacy?
Yunpeng Zhao, Jie Zhang
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
Shlomi Hod, Lucas Rosenblatt, Julia Stoyanovich
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
Yanming Liu, Xinyue Peng, Yuwei Zhang et al.
Empirical Privacy Variance
Yuzheng Hu, Fan Wu, Ruicheng Xian et al.
Exploiting Hidden Symmetry to Improve Objective Perturbation for DP Linear Learners with a Nonsmooth L1-Norm
Du Chen, Geoffrey A. Chua
Federated Binary Matrix Factorization Using Proximal Optimization
Sebastian Dalleiger, Jilles Vreeken, Michael Kamp
Federated t-SNE and UMAP for Distributed Data Visualization
Dong Qiao, Xinxian Ma, Jicong Fan
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection
Lei Shen, Zhenheng Tang, Lijun Wu et al.
Instance-Optimality for Private KL Distribution Estimation
Jiayuan Ye, Vitaly Feldman, Kunal Talwar
Multi-Class Support Vector Machine with Differential Privacy
Jinseong Park, Yujin Choi, Jaewook Lee
Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
Maryam Aliakbarpour, Zhan Shi, Ria Stevens et al.
Online robust locally differentially private learning for nonparametric regression
Chenfei Gu, Qiangqiang Zhang, Ting Li et al.
On the Sample Complexity of Differentially Private Policy Optimization
Yi He, Xingyu Zhou
Optimal Best Arm Identification under Differential Privacy
Marc Jourdan, Achraf Azize
Optimal Regret of Bandits under Differential Privacy
Achraf Azize, Yulian Wu, Junya Honda et al.
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Linh Tran, Wei Sun, Stacy Patterson et al.
Private Continual Counting of Unbounded Streams
Ben Jacobsen, Kassem Fawaz
Private Hyperparameter Tuning with Ex-Post Guarantee
Badih Ghazi, Pritish Kamath, Alexander Knop et al.
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
Haoteng Yin, Rongzhe Wei, Eli Chien et al.
Private Mechanism Design via Quantile Estimation
Yuanyuan Yang, Tao Xiao, Bhuvesh Kumar et al.
Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
Bo Li, Wei Wang, Peng Ye
Private Set Union with Multiple Contributions
Travis Dick, Haim Kaplan, Alex Kulesza et al.
Private Training Large-scale Models with Efficient DP-SGD
Liangyu Wang, Junxiao Wang, Jie Ren et al.
PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy
Ripan Kumar Kundu, Istiak Ahmed, Khaza Anuarul Hoque
Purifying Approximate Differential Privacy with Randomized Post-processing
Yingyu Lin, Erchi Wang, Yian Ma et al.
Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML
Ryan McKenna
Sketched Gaussian Mechanism for Private Federated Learning
Qiaobo Li, Zhijie Chen, Arindam Banerjee
Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency
Xinyu He, Dongqi Fu, Hanghang Tong et al.
Towards hyperparameter-free optimization with differential privacy
Ruixuan Liu, Zhiqi Bu
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
Bogdan Kulynych, Juan Gomez, Georgios Kaissis et al.
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Yue Niu, Ramy E. Ali, Saurav Prakash et al.
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar et al.
Auditing Private Prediction
Karan Chadha, Matthew Jagielski, Nicolas Papernot et al.