Fetching the paper…
Reading the bibliography…
Differential privacy is a widely used notion of security that enables the processing of sensitive information.
Estimation des densités: risque minimax
Jean Bretagnolle and Catherine Huber · 1978
Earlier work this paper cites.
Quantum statistical query learning, 2020
Srinivasan Arunachalam, Alex B. Grilo, and Henry Yuen · 2002
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
How to break anonymity of the netflix prize dataset, 2007
Arvind Narayanan and Vitaly Shmatikov · 2007
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Channel coding rate in the finite blocklength regime
Yury Polyanskiy, H. Vincent Poor, and Sergio Verdu · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
On the quantum rényi relative entropies and related capacity formulas
Milán Mosonyi and Fumio Hiai · 2011
Earlier work this paper cites.
On the strong converses for the quantum channel capacity theorems
Naresh Sharma and Naqueeb Ahmad Warsi · 2012
Earlier work this paper cites.
On quantum rényi entropies: A new generalization and some properties
Martin Müller-Lennert, Frédéric Dupuis, Oleg Szehr, Serge Fehr, and Marco Tomamichel · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Ré nyi divergence and kullback-leibler divergence
Tim van Erven and Peter Harremoes · 2014
Earlier work this paper cites.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
Earlier work this paper cites.
Quantum information processing with finite resources: mathematical foundations , volume 5
Marco Tomamichel · 2015
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.
Learning with differential privacy: Stability, learnability and the sufficiency and necessity of erm principle
Yu-Xiang Wang, Jing Lei, and Stephen E. Fienberg · 2016
Earlier work this paper cites.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Earlier work this paper cites.
Generalized coherent states, reproducing kernels, and quantum support vector machines
Rupak Chatterjee and Ting Yu · 2016
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data, 2017
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Earlier work this paper cites.
Generalization for adaptively-chosen estimators via stable median
Vitaly Feldman and Thomas Steinke · 2017
Cited alongside, same era.
Privacy-preserving quantum machine learning using differential privacy
Makhamisa Senekane, Mhlambululi Mafu, and Benedict Molibeli Taele · 2017
Cited alongside, same era.
Differential privacy in quantum computation
Li Zhou and Mingsheng Ying · 2017
Cited alongside, same era.
The Complexity of Differential Privacy , pages 347–450
Salil Vadhan · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Cs7880: Rigorous approaches to data privacy, 2017
Jonathan Ullman · 2017
Cited alongside, same era.
Quantum federated learning through blind quantum computing
Weikang Li, Sirui Lu, and Dong-Ling Deng · 2021
Later among the works it cites.
The quantum wasserstein distance of order 1
Giacomo De Palma, Milad Marvian, Dario Trevisan, and Seth Lloyd · 2021
Later among the works it cites.
Mathematical comparison of classical and quantum mechanisms in optimization under local differential privacy, 2021
Yuuya Yoshida · 2021
Later among the works it cites.
Quantum noise protects quantum classifiers against adversaries
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, and Nana Liu · 2021
Later among the works it cites.
Cost function dependent barren plateaus in shallow parametrized quantum circuits
Marco Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles · 2021
Later among the works it cites.
Noise-induced barren plateaus in variational quantum algorithms
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Raef Bassily, Om Thakkar, and Abhradeep Thakurta · 2018
Cited alongside, same era.
Quantum Computing in the NISQ era and beyond
John Preskill · 2018
Cited alongside, same era.
Approximate and probabilistic differential privacy definitions
Sebastian Meiser · 2018
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Cited alongside, same era.
Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension, 2018
András Gilyén, Seth Lloyd, and Ewin Tang · 2018
Cited alongside, same era.
Quantum-inspired sublinear classical algorithms for solving low-rank linear systems, 2018
Nai-Hui Chia, Han-Hsuan Lin, and Chunhao Wang · 2018
Cited alongside, same era.
Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J. Coles · 2021
Later among the works it cites.
Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions
Ewin Tang · 2021
Later among the works it cites.
Limitations of optimization algorithms on noisy quantum devices
Daniel Stilck França and Raul García-Patrón · 2021
Later among the works it cites.
Supervised quantum machine learning models are kernel methods, 2021
Maria Schuld · 2021
Later among the works it cites.
Quantum differentially private sparse regression learning
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, and Dacheng Tao · 2022
Later among the works it cites.
Quantum local differential privacy and quantum statistical query model
Armando Angrisani and Elham Kashefi · 2022
Later among the works it cites.
A short note on an inequality between kl and tv, 2022
Clément L. Canonne · 2022
Later among the works it cites.
Challenges towards the next frontier in privacy
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, et al · 2023
Closest in time.
Quantum machine learning with differential privacy
William M Watkins, Samuel Yen-Chi Chen, and Shinjae Yoo · 2023
Closest in time.
Privacy against hypothesis-testing adversaries for quantum computing, 2023
Farhad Farokhi · 2023
Closest in time.
Quantum pufferfish privacy: A flexible privacy framework for quantum systems, 2023
Theshani Nuradha, Ziv Goldfeld, and Mark M. Wilde · 2023
Closest in time.
Limitations of variational quantum algorithms: a quantum optimal transport approach
Giacomo De Palma, Milad Marvian, Cambyse Rouzé, and Daniel Stilck França · 2023
Closest in time.
Benefits and detriments of noise in quantum classification
Christoph Hirche · 2023
Closest in time.
Certified robustness of quantum classifiers against adversarial examples through quantum noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, and Sy-Yen Kuo · 2023
Closest in time.
Quantum r \ \backslash ’enyi and f f -divergences from integral representations
Christoph Hirche and Marco Tomamichel · 2023
Closest in time.
Robustness of quantum algorithms against coherent control errors, 2023
J. Berberich, D. Fink, and C. Holm · 2023
Closest in time.