
Computational TwoParty Correlation: A Dichotomy for KeyAgreement Protocols
Let π be an efficient twoparty protocol that given security parameter κ...
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The Sample Complexity of DistributionFree Parity Learning in the Robust Shuffle Model
We provide a lowerbound on the sample complexity of distributionfree pa...
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Separating Adaptive Streaming from Oblivious Streaming
We present a streaming problem for which every adversariallyrobust stre...
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Modernizing Data Control: Making Personal Digital Data Mutually Beneficial for Citizens and Industry
We are entering a new "data everywhereanytime" era that pivots us from ...
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On the Round Complexity of the Shuffle Model
The shuffle model of differential privacy was proposed as a viable model...
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Private Summation in the MultiMessage Shuffle Model
The shuffle model of differential privacy (Erlingsson et al. SODA 2019; ...
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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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The Complexity of Verifying Loopfree Programs as Differentially Private
We study the problem of verifying differential privacy for loopfree pro...
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The Complexity of Verifying Circuits as Differentially Private
We study the problem of verifying differential privacy for straight line...
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Improved Summation from Shuffling
A protocol by Ishai et al. (FOCS 2006) showing how to implement distribu...
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Differentially Private Summation with MultiMessage Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for np...
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Exploring Differential Obliviousness
In a recent paper Chan et al. [SODA '19] proposed a relaxation of the no...
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Towards Formalizing the GDPR's Notion of Singling Out
There is a significant conceptual gap between legal and mathematical thi...
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The Privacy Blanket of the Shuffle Model
This work studies differential privacy in the context of the recently pr...
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Private Center Points and Learning of Halfspaces
We present a private learner for halfspaces over an arbitrary finite dom...
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Linear Program Reconstruction in Practice
We briefly report on a linear program reconstruction attack performed on...
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The Limits of PostSelection Generalization
While statistics and machine learning offers numerous methods for ensuri...
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Segmentation, Incentives and Privacy
Data driven segmentation is the powerhouse behind the success of online ...
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Private Incremental Regression
Data is continuously generated by modern data sources, and a recent chal...
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Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
We compare the sample complexity of private learning [Kasiviswanathan et...
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Kobbi Nissim
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