Diffprivlib: The IBM Differential Privacy Library

4 July 2019

Papers citing "Diffprivlib: The IBM Differential Privacy Library"

30 / 30 papers shown

Title
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data Georgi Ganev Meenatchi Sundaram Muthu Selva Annamalai Sofiane Mahiou Emiliano De Cristofaro 24 2 0 09 Apr 2025
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 49 2 0 19 Aug 2024
Lomas: A Platform for Confidential Analysis of Private Data Damien Aymon Dan-Thuy Lam Lancelot Marti Pauline Maury-Laribiere Christine Choirat R. D. Fondeville PILM 15 2 0 24 Jun 2024
Unified Locational Differential Privacy Framework Aman Priyanshu Yash Maurya Suriya Ganesh Vy Tran 31 0 0 06 May 2024
FastLloyd: Federated, Accurate, Secure, and Tunable $k$ -Means Clustering with Differential Privacy Abdulrahman Diaa Thomas Humphries Florian Kerschbaum FedML 38 0 0 03 May 2024
You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks Qiushi Li Yan Zhang Ju Ren Qi Li Yaoxue Zhang AAML PICV 41 23 0 05 Apr 2024
Programming Frameworks for Differential Privacy Marco Gaboardi Michael Hay Salil P. Vadhan 38 1 0 17 Mar 2024
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting Asif Iqbal P. Gope Biplab Sikdar 39 2 0 03 Mar 2024
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation Dzung Pham Shreyas Kulkarni Amir Houmansadr 33 0 0 29 Oct 2023
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning S. Giddens F. Liu 38 1 0 19 Sep 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 49 11 0 11 Aug 2023
Random Number Generators and Seeding for Differential Privacy N. Holohan SyDa 27 1 0 07 Jul 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 28 18 0 28 Feb 2023
A General Framework for Auditing Differentially Private Machine Learning Fred Lu Joseph Munoz Maya Fuchs Tyler LeBlond Elliott Zaresky-Williams Edward Raff Francis Ferraro Brian Testa FedML 22 35 0 16 Oct 2022
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation Sofiane Mahiou Kai Xu Georgi Ganev SyDa 13 11 0 12 Jul 2022
Exploring the Unfairness of DP-SGD Across Settings Frederik Noe R. Herskind Anders Søgaard 27 4 0 24 Feb 2022
Plume: Differential Privacy at Scale Kareem Amin Jennifer Gillenwater Matthew Joseph Alex Kulesza Sergei Vassilvitskii 34 9 0 27 Jan 2022
Differential Privacy Made Easy Muhammad Aitsam SyDa 37 8 0 01 Jan 2022
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 22 8 0 25 Oct 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 37 57 0 23 Sep 2021
An automatic differentiation system for the age of differential privacy Dmitrii Usynin Alexander Ziller Moritz Knolle Andrew Trask Kritika Prakash Daniel Rueckert Georgios Kaissis 35 3 0 22 Sep 2021
Secure Random Sampling in Differential Privacy N. Holohan S. Braghin 18 16 0 21 Jul 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori R. Caruana Zhiqi Bu J. Shen Janardhan Kulkarni 33 37 0 17 Jun 2021
DDUO: General-Purpose Dynamic Analysis for Differential Privacy Chiké Abuah Alex Silence David Darais Joseph P. Near 51 12 0 16 Mar 2021
Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining Gamal Elkoumy A. Pankova Marlon Dumas 30 6 0 02 Dec 2020
Private Reinforcement Learning with PAC and Regret Guarantees G. Vietri Borja Balle A. Krishnamurthy Zhiwei Steven Wu 23 59 0 18 Sep 2020
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 16 5 0 26 Jul 2020
Reducing Risk of Model Inversion Using Privacy-Guided Training Abigail Goldsteen Gilad Ezov Ariel Farkash 27 4 0 29 Jun 2020
Disparate Vulnerability to Membership Inference Attacks B. Kulynych Mohammad Yaghini Giovanni Cherubin Michael Veale Carmela Troncoso 13 39 0 02 Jun 2019
Optimal Differentially Private Mechanisms for Randomised Response N. Holohan D. Leith O. Mason 32 62 0 16 Dec 2016