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Found 27 papers, 2 papers with code
Ticketed Learning-Unlearning Schemes
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.
On User-Level Private Convex Optimization
We introduce a new mechanism for stochastic convex optimization (SCO) with user-level differential privacy guarantees.
Regression with Label Differential Privacy
We study the task of training regression models with the guarantee of label differential privacy (DP).
Private Ad Modeling with DP-SGD
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD).
Anonymized Histograms in Intermediate Privacy Models
We study the problem of privately computing the anonymized histogram (a. k. a.
Private Isotonic Regression
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.
Algorithms with More Granular Differential Privacy Guarantees
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.
Faster Privacy Accounting via Evolving Discretization
We introduce a new algorithm for numerical composition of privacy random variables, useful for computing the accurate differential privacy parameters for composition of mechanisms.
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions
The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mechanism in the context of differential privacy (DP).
User-Level Differentially Private Learning via Correlated Sampling
Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.
User-Level Private Learning via Correlated Sampling
Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.
Large-Scale Differentially Private BERT
In this work, we study the large-scale pretraining of BERT-Large with differentially private SGD (DP-SGD).
Locally Private k-Means in One Round
We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP).
Deep Learning with Label Differential Privacy
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.
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries
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.
Sample-efficient proper PAC learning with approximate differential privacy
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.
Robust and Private Learning of Halfspaces
In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces.
On Distributed Differential Privacy and Counting Distinct Elements
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 $n$.
Differentially Private Clustering: Tight Approximation Ratios
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.
Near-tight closure bounds for Littlestone and threshold dimensions
We study closure properties for the Littlestone and threshold dimensions of binary hypothesis classes.
Advances and Open Problems in Federated Learning
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.
Private Aggregation from Fewer Anonymous Messages
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
On the Power of Multiple Anonymous Messages
- 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.
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods.
Recursive Sketches for Modular Deep Learning
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.
On the Power of Learning from $k$-Wise Queries
Hence it is natural to ask whether algorithms using $k$-wise queries can solve learning problems more efficiently and by how much.
The Optimality of Correlated Sampling
In this note, we give a surprisingly simple proof that this protocol is in fact tight.
Computational Complexity Information Theory Information Theory
