Eliad Tsfadia
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View article: Fair Coin Flipping: Tighter Analysis and the Many-Party Case
Fair Coin Flipping: Tighter Analysis and the Many-Party Case Open
In a multi-party fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some adversarial parties try to bias the output. In this work, we focus on the case of an arbitrary number of corrupted parties. C…
View article: Mildly Accurate Computationally Differentially Private Inner Product Protocols Imply Oblivious Transfer
Mildly Accurate Computationally Differentially Private Inner Product Protocols Imply Oblivious Transfer Open
In distributed differential privacy, multiple parties collaboratively analyze their combined data while protecting the privacy of each party's data from the eyes of the others. Interestingly, for certain fundamental two-party functions lik…
View article: Data Reconstruction: When You See It and When You Don't
Data Reconstruction: When You See It and When You Don't Open
We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent th…
View article: Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes Open
Fingerprinting arguments, first introduced by Bun, Ullman, and Vadhan (STOC 2014), are the most widely used method for establishing lower bounds on the sample complexity or error of approximately differentially private (DP) algorithms. Sti…
View article: Adaptive Data Analysis in a Balanced Adversarial Model
Adaptive Data Analysis in a Balanced Adversarial Model Open
In adaptive data analysis, a mechanism gets $n$ i.i.d. samples from an unknown distribution $D$, and is required to provide accurate estimations to a sequence of adaptively chosen statistical queries with respect to $D$. Hardt and Ullman (…
View article: Differentially-Private Clustering of Easy Instances
Differentially-Private Clustering of Easy Instances Open
Clustering is a fundamental problem in data analysis. In differentially private clustering, the goal is to identify $k$ cluster centers without disclosing information on individual data points. Despite significant research progress, the pr…
View article: FriendlyCore: Practical Differentially Private Aggregation
FriendlyCore: Practical Differentially Private Aggregation Open
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. We…
View article: On the Complexity of Two-Party Differential Privacy
On the Complexity of Two-Party Differential Privacy Open
In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, th…
View article: A Tight Parallel Repetition Theorem for Partially Simulatable Interactive Arguments via Smooth KL-Divergence
A Tight Parallel Repetition Theorem for Partially Simulatable Interactive Arguments via Smooth KL-Divergence Open
Hardness amplification is a central problem in the study of interactive protocols. While ``natural'' parallel repetition transformation is known to reduce the soundness error of some special cases of interactive arguments: three-message pr…
View article: An Almost-Optimally Fair Three-Party Coin-Flipping Protocol
An Almost-Optimally Fair Three-Party Coin-Flipping Protocol Open
In a multiparty fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some corrupted parties try to bias the output. Cleve [STOC 1986] has shown that in the case of dishonest majority (i.e., at least h…
View article: Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Open
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 …
View article: It Takes Two to #MeToo - Using Enclaves to Build Autonomous Trusted Systems
It Takes Two to #MeToo - Using Enclaves to Build Autonomous Trusted Systems Open
We provide enhanced security against insider attacks in services that manage extremely sensitive data. One example is a #MeToo use case where sexual harassment complaints are reported but only revealed when another complaint is filed again…
View article: Securing the Storage Data Path with SGX Enclaves
Securing the Storage Data Path with SGX Enclaves Open
We explore the use of SGX enclaves as a means to improve the security of handling keys and data in storage systems. We study two main configurations for SGX computations, as they apply to performing data-at-rest encryption in a storage sys…
View article: Fair coin flipping: tighter analysis and the many-party case
Fair coin flipping: tighter analysis and the many-party case Open
In a multi-party fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some corrupted parties try to bias the output. In this work we focus on the case of dishonest majority, ie at least half of the pa…
View article: Fair Coin Flipping: Tighter Analysis and the Many-Party Case
Fair Coin Flipping: Tighter Analysis and the Many-Party Case Open
In a multi-party fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some adversarial parties try to bias the output. In this work we focus on the case of an arbitrary number of corrupted parties. Cl…