Search Results for author: Uri Stemmer

Found 40 papers, 1 papers with code

On Differentially Private Online Predictions

no code implementations27 Feb 2023 Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive.

On Differential Privacy and Adaptive Data Analysis with Bounded Space

no code implementations11 Feb 2023 Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou

We study the space complexity of the two related fields of differential privacy and adaptive data analysis.

Concurrent Shuffle Differential Privacy Under Continual Observation

no code implementations29 Jan 2023 Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

the counter problem) and show that the concurrent shuffle model allows for significantly improved error compared to a standard (single) shuffle model.

Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model

no code implementations22 Jan 2023 Menachem Sadigurschi, Moshe Shechner, Uri Stemmer

Streaming algorithms are typically analyzed in the oblivious setting, where we assume that the input stream is fixed in advance.

Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs

no code implementations3 Jul 2022 Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer

When inputs are adaptive, however, an adversarial input can be constructed after $O(\ell)$ queries with the classic estimator and the best known robust estimator only supports $\tilde{O}(\ell^2)$ queries.

Dimensionality Reduction

On the Robustness of CountSketch to Adaptive Inputs

no code implementations28 Feb 2022 Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer

CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements.

Dimensionality Reduction

Monotone Learning

no code implementations10 Feb 2022 Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer

Our transformation readily implies monotone learners in a variety of contexts: for example it extends Pestov's result to classification tasks with an arbitrary number of labels.

Binary Classification Classification +1

Adaptive Data Analysis with Correlated Observations

no code implementations21 Jan 2022 Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer

The vast majority of the work on adaptive data analysis focuses on the case where the samples in the dataset are independent.

FriendlyCore: Practical Differentially Private Aggregation

no code implementations19 Oct 2021 Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer

Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large number of data points that is required for accurate results.


Differentially Private Approximate Quantiles

no code implementations11 Oct 2021 Haim Kaplan, Shachar Schnapp, Uri Stemmer

In this work we study the problem of differentially private (DP) quantiles, in which given dataset $X$ and quantiles $q_1, ..., q_m \in [0, 1]$, we want to output $m$ quantile estimations which are as close as possible to the true quantiles and preserve DP.

A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

no code implementations30 Jul 2021 Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance.

On the Sample Complexity of Privately Learning Axis-Aligned Rectangles

1 code implementation NeurIPS 2021 Menachem Sadigurschi, Uri Stemmer

We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid $X^d\subseteq{\mathbb{R}}^d$ with differential privacy.

Differentially Private Multi-Armed Bandits in the Shuffle Model

no code implementations NeurIPS 2021 Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

We give an $(\varepsilon,\delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\sum_{a\in [k]:\Delta_a>0}\frac{\log T}{\Delta_a}\right)+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, and a distribution-independent regret of $O\left(\sqrt{kT\log T}+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, where $T$ is the number of rounds, $\Delta_a$ is the suboptimality gap of the arm $a$, and $k$ is the total number of arms.

Multi-Armed Bandits

Separating Adaptive Streaming from Oblivious Streaming

no code implementations26 Jan 2021 Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer

We present a streaming problem for which every adversarially-robust streaming algorithm must use polynomial space, while there exists a classical (oblivious) streaming algorithm that uses only polylogarithmic space.

Data Structures and Algorithms

Differentially Private Weighted Sampling

no code implementations25 Oct 2020 Edith Cohen, Ofir Geri, Tamas Sarlos, Uri Stemmer

A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics.

The Sparse Vector Technique, Revisited

no code implementations2 Oct 2020 Haim Kaplan, Yishay Mansour, Uri Stemmer

This simple algorithm privately tests whether the value of a given query on a database is close to what we expect it to be.

On the Round Complexity of the Shuffle Model

no code implementations28 Sep 2020 Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer

Combining this primitive with the two-round semi-honest protocol of Applebaun et al. [TCC 2018], we obtain that every randomized functionality can be computed in the shuffle model with an honest majority, in merely two rounds.

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity

no code implementations NeurIPS 2020 Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

We present a differentially private learner for halfspaces over a finite grid $G$ in $\mathbb{R}^d$ with sample complexity $\approx d^{2. 5}\cdot 2^{\log^*|G|}$, which improves the state-of-the-art result of [Beimel et al., COLT 2019] by a $d^2$ factor.

Adversarially Robust Streaming Algorithms via Differential Privacy

no code implementations NeurIPS 2020 Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary.

Adversarial Robustness

How to Find a Point in the Convex Hull Privately

no code implementations30 Mar 2020 Haim Kaplan, Micha Sharir, Uri Stemmer

We study the question of how to compute a point in the convex hull of an input set $S$ of $n$ points in ${\mathbb R}^d$ in a differentially private manner.

Closure Properties for Private Classification and Online Prediction

no code implementations10 Mar 2020 Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer

Let~$\cH$ be a class of boolean functions and consider a {\it composed class} $\cH'$ that is derived from~$\cH$ using some arbitrary aggregation rule (for example, $\cH'$ may be the class of all 3-wise majority-votes of functions in $\cH$).

Classification General Classification +1

The power of synergy in differential privacy: Combining a small curator with local randomizers

no code implementations18 Dec 2019 Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer

Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al.,\ USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users.

Two-sample testing

Privately Learning Thresholds: Closing the Exponential Gap

no code implementations22 Nov 2019 Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer

This problem has received much attention recently; unlike the non-private case, where the sample complexity is independent of the domain size and just depends on the desired accuracy and confidence, for private learning the sample complexity must depend on the domain size $X$ (even for approximate differential privacy).

Locally Private k-Means Clustering

no code implementations4 Jul 2019 Uri Stemmer

We design a new algorithm for the Euclidean $k$-means problem that operates in the local model of differential privacy.


Private Center Points and Learning of Halfspaces

no code implementations27 Feb 2019 Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer

The building block for this learner is a differentially private algorithm for locating an approximate center point of $m>\mathrm{poly}(d, 2^{\log^*|X|})$ points -- a high dimensional generalization of the median function.

Differentially Private Learning of Geometric Concepts

no code implementations13 Feb 2019 Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex).

PAC learning

The Limits of Post-Selection Generalization

no code implementations NeurIPS 2018 Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, Jonathan Ullman

While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the choice of analysis depends on previous interactions with the same dataset.

Concentration Bounds for High Sensitivity Functions Through Differential Privacy

no code implementations6 Mar 2017 Kobbi Nissim, Uri Stemmer

We show that differential privacy can be used to prove concentration bounds for such functions in the non-adaptive setting.

Vocal Bursts Intensity Prediction

Locating a Small Cluster Privately

no code implementations19 Apr 2016 Kobbi Nissim, Uri Stemmer, Salil Vadhan

We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim, and Smith, 2006].


Simultaneous Private Learning of Multiple Concepts

no code implementations27 Nov 2015 Mark Bun, Kobbi Nissim, Uri Stemmer

We investigate the direct-sum problem in the context of differentially private PAC learning: What is the sample complexity of solving $k$ learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving $k$ learning tasks without privacy?

PAC learning

Algorithmic Stability for Adaptive Data Analysis

no code implementations8 Nov 2015 Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, Jonathan Ullman

Specifically, suppose there is an unknown distribution $\mathbf{P}$ and a set of $n$ independent samples $\mathbf{x}$ is drawn from $\mathbf{P}$.

Differentially Private Release and Learning of Threshold Functions

no code implementations28 Apr 2015 Mark Bun, Kobbi Nissim, Uri Stemmer, Salil Vadhan

Our sample complexity upper and lower bounds also apply to the tasks of learning distributions with respect to Kolmogorov distance and of properly PAC learning thresholds with differential privacy.

PAC learning

On the Generalization Properties of Differential Privacy

no code implementations22 Apr 2015 Kobbi Nissim, Uri Stemmer

Very recently, Bassily et al. presented an improved bound and showed that (a variant of) the private multiplicative weights algorithm can answer $k$ adaptively chosen statistical queries using sample complexity that grows logarithmically in $k$.

Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

no code implementations10 Jul 2014 Amos Beimel, Kobbi Nissim, Uri Stemmer

We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.

Learning Privately with Labeled and Unlabeled Examples

no code implementations10 Jul 2014 Amos Beimel, Kobbi Nissim, Uri Stemmer

In 2008, Kasiviswanathan et al. (FOCS 2008) gave a generic construction of private learners, in which the sample complexity is (generally) higher than what is needed for non-private learners.

Active Learning

Characterizing the Sample Complexity of Private Learners

no code implementations10 Feb 2014 Amos Beimel, Kobbi Nissim, Uri Stemmer

Kasiviswanathan et al. gave a generic construction of private learners for (finite) concept classes, with sample complexity logarithmic in the size of the concept class.

PAC learning

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