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no code implementations • 27 Jun 2023 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

Subsequently, given any subset of examples that wish to be unlearnt, the goal is to learn, without the knowledge of the original training dataset, a good predictor that is identical to the predictor that would have been produced when learning from scratch on the surviving examples.

no code implementations • 8 May 2023 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang

We introduce a new mechanism for stochastic convex optimization (SCO) with user-level differential privacy guarantees.

no code implementations • 12 Dec 2022 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

We study the task of training regression models with the guarantee of label differential privacy (DP).

no code implementations • 21 Nov 2022 • Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V Varadarajan, Chiyuan Zhang

A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD).

no code implementations • 27 Oct 2022 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

We study the problem of privately computing the anonymized histogram (a. k. a.

no code implementations • 27 Oct 2022 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

For the most general problem of isotonic regression over a partially ordered set (poset) $\mathcal{X}$ and for any Lipschitz loss function, we obtain a pure-DP algorithm that, given $n$ input points, has an expected excess empirical risk of roughly $\mathrm{width}(\mathcal{X}) \cdot \log|\mathcal{X}| / n$, where $\mathrm{width}(\mathcal{X})$ is the width of the poset.

no code implementations • 8 Sep 2022 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke

Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed.

no code implementations • 10 Jul 2022 • Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

We introduce a new algorithm for numerical composition of privacy random variables, useful for computing the accurate differential privacy parameters for composition of mechanisms.

no code implementations • 10 Jul 2022 • Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mechanism in the context of differential privacy (DP).

no code implementations • NeurIPS 2021 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.

no code implementations • 21 Oct 2021 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.

no code implementations • 3 Aug 2021 • Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi

In this work, we study the large-scale pretraining of BERT-Large with differentially private SGD (DP-SGD).

no code implementations • 20 Apr 2021 • Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP).

no code implementations • NeurIPS 2021 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy guarantees.

no code implementations • 16 Dec 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

On the other hand, the algorithm of Dagan and Kur has a remarkable advantage that the $\ell_{\infty}$ error bound of $O(\frac{1}{\epsilon}\sqrt{k \log \frac{1}{\delta}})$ holds not only in expectation but always (i. e., with probability one) while we can only get a high probability (or expected) guarantee on the error.

no code implementations • 7 Dec 2020 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension $d$ with approximate differential privacy is $\tilde O(d^6)$, ignoring privacy and accuracy parameters.

no code implementations • 30 Nov 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces.

no code implementations • 21 Sep 2020 • Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

We study the setup where each of $n$ users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of $(\epsilon, \delta)$-differentially privacy: - In the non-interactive local setting, we prove that the additive error of any protocol is $\Omega(n)$ for any constant $\epsilon$ and for any $\delta$ inverse polynomial in $n$.

no code implementations • NeurIPS 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

For several basic clustering problems, including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient differentially private algorithms that achieve essentially the same approximation ratios as those that can be obtained by any non-private algorithm, while incurring only small additive errors.

no code implementations • 7 Jul 2020 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

We study closure properties for the Littlestone and threshold dimensions of binary hypothesis classes.

8 code implementations • 10 Dec 2019 • Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.

no code implementations • 24 Sep 2019 • Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker

Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, \delta\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $\delta = \frac{1}{\mathrm{poly}(n)}$, incurs only a constant error and requires only a constant number of messages per party.

Cryptography and Security Data Structures and Algorithms

no code implementations • 29 Aug 2019 • Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker

- Protocols in the multi-message shuffled model with $poly(\log{B}, \log{n})$ bits of communication per user and $poly\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms.

no code implementations • 19 Jun 2019 • Badih Ghazi, Rasmus Pagh, Ameya Velingker

Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods.

no code implementations • 29 May 2019 • Badih Ghazi, Rina Panigrahy, Joshua R. Wang

The sketch summarizes essential information about the inputs and outputs of the network and can be used to quickly identify key components and summary statistics of the inputs.

no code implementations • 28 Feb 2017 • Vitaly Feldman, Badih Ghazi

Hence it is natural to ask whether algorithms using $k$-wise queries can solve learning problems more efficiently and by how much.

1 code implementation • 4 Dec 2016 • Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan

In this note, we give a surprisingly simple proof that this protocol is in fact tight.

Computational Complexity Information Theory Information Theory

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