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05/24/2023
Adaptive Data Analysis in a Balanced Adversarial Model
In adaptive data analysis, a mechanism gets n i.i.d. samples from an unk...
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05/16/2023
Private Everlasting Prediction
A private learner is trained on a sample of labeled points and generates...
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02/27/2023
On Differentially Private Online Predictions
In this work we introduce an interactive variant of joint differential p...
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11/07/2021
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds
A dynamic algorithm against an adaptive adversary is required to be corr...
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05/03/2021
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols
Let π be an efficient two-party protocol that given security parameter κ...
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03/29/2021
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model
We provide a lowerbound on the sample complexity of distribution-free pa...
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01/26/2021
Separating Adaptive Streaming from Oblivious Streaming
We present a streaming problem for which every adversarially-robust stre...
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12/15/2020
Modernizing Data Control: Making Personal Digital Data Mutually Beneficial for Citizens and Industry
We are entering a new "data everywhere-anytime" era that pivots us from ...
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09/28/2020
On the Round Complexity of the Shuffle Model
The shuffle model of differential privacy was proposed as a viable model...
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02/03/2020
Private Summation in the Multi-Message Shuffle Model
The shuffle model of differential privacy (Erlingsson et al. SODA 2019; ...
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12/18/2019
The power of synergy in differential privacy:Combining a small curator with local randomizers
Motivated by the desire to bridge the utility gap between local and trus...
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11/08/2019
The Complexity of Verifying Loop-free Programs as Differentially Private
We study the problem of verifying differential privacy for loop-free pro...
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11/08/2019
The Complexity of Verifying Circuits as Differentially Private
We study the problem of verifying differential privacy for straight line...
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09/24/2019
Improved Summation from Shuffling
A protocol by Ishai et al. (FOCS 2006) showing how to implement distribu...
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06/20/2019
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for n-p...
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05/03/2019
Exploring Differential Obliviousness
In a recent paper Chan et al. [SODA '19] proposed a relaxation of the no...
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04/12/2019
Towards Formalizing the GDPR's Notion of Singling Out
There is a significant conceptual gap between legal and mathematical thi...
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03/07/2019
The Privacy Blanket of the Shuffle Model
This work studies differential privacy in the context of the recently pr...
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02/27/2019
Private Center Points and Learning of Halfspaces
We present a private learner for halfspaces over an arbitrary finite dom...
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10/12/2018
Linear Program Reconstruction in Practice
We briefly report on a linear program reconstruction attack performed on...
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06/15/2018
The Limits of Post-Selection Generalization
While statistics and machine learning offers numerous methods for ensuri...
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06/04/2018
Segmentation, Incentives and Privacy
Data driven segmentation is the powerhouse behind the success of online ...
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01/04/2017
Private Incremental Regression
Data is continuously generated by modern data sources, and a recent chal...
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07/10/2014
