Fetching the paper…
Reading the bibliography…
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML).
Generalized intervals and the dependency problem
Walter Krämer · 2006
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Lipschitz extensions for node-private graph statistics and the generalized exponential mechanism
Sofya Raskhodnikova and Adam Smith · 2016
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
Earlier work this paper cites.
Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
Earlier work this paper cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman and Aladin Virmaux · 2018
Earlier work this paper cites.
A simplicial homology algorithm for lipschitz optimisation
Stefan C Endres, Carl Sandrock, and Walter W Focke · 2018
Earlier work this paper cites.
Diffprivlib: The ibm differential privacy library, 2019
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
Earlier work this paper cites.
On the effectiveness of interval bound propagation for training verifiably robust models, 2019
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2019
Earlier work this paper cites.
Exploring the use of lipschitz neural networks for automating the design of differentially private mechanisms
Yonadav Shavit and Boriana Gjura · 2019
Cited alongside, same era.
Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
Cited alongside, same era.
Recurjac: An efficient recursive algorithm for bounding jacobian matrix of neural networks and its applications
Huan Zhang, Pengchuan Zhang, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Lipschitz-certifiable training with a tight outer bound
Sungyoon Lee, Jaewook Lee, and Saerom Park · 2020
Cited alongside, same era.
Available from google/differential-privacy , 2021
Differential Privacy · 2021
Closest in time.
Lipbab: Computing exact lipschitz constant of relu networks
Aritra Bhowmick, Meenakshi D’Souza, and G Srinivasa Raghavan · 2021
Closest in time.
Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
Closest in time.
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus Makowski, Daniel Rueckert, and Georgios Kaissis · 2021
Closest in time.
Swift for tensorflow: A portable, flexible platform for deep learning, 2021
Brennan Saeta, Denys Shabalin, Marc Rasi, Brad Larson, Xihui Wu, Parker Schuh, Michelle Casbon, Daniel Zheng, Saleem Abdulrasool, Aleksandr Efremov, Dave Abrahams, Chris Lattner, and Richard Wei · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vitaly Feldman and Tijana Zrnic · 2020
Cited alongside, same era.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson · 2020
Cited alongside, same era.
Towards general-purpose infrastructure for protecting scientific data under study
Andrew Trask and Kritika Prakash · 2020
Cited alongside, same era.
Available from opacus.ai , 2021
Opacus PyTorch library · 2021
Cited alongside, same era.
Available from TensorFlow Privacy , 2021
TensorFlow Privacy · 2021
Cited alongside, same era.
Pysyft: A library for easy federated learning
Alexander Ziller, Andrew Trask, Antonio Lopardo, Benjamin Szymkow, Bobby Wagner, Emma Bluemke, Jean-Mickael Nounahon, Jonathan Passerat-Palmbach, Kritika Prakash, Nick Rose, et al · 2021
Closest in time.
Syft 0.5: A platform for universally deployable structured transparency
Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, et al · 2021
Closest in time.
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
Closest in time.
Dduo: General-purpose dynamic analysis for differential privacy
Chike Abuah, Alex Silence, David Darais, and Joe Near · 2021
Closest in time.
From identity to difference: A quantitative interpretation of the identity type
Paolo Pistone · 2021
Closest in time.