Search Results for author: Aleksandra Korolova

Found 9 papers, 2 papers with code

Robust Allocations with Diversity Constraints

no code implementations NeurIPS 2021 Zeyu Shen, Lodewijk Gelauff, Ashish Goel, Aleksandra Korolova, Kamesh Munagala

We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion.

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

Preference-Informed Fairness

no code implementations3 Apr 2019 Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona

We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of both individual fairness and envy-freeness.

Decision Making Fairness

The Power of The Hybrid Model for Mean Estimation

no code implementations29 Nov 2018 Brendan Avent, Yatharth Dubey, Aleksandra Korolova

We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees.

Differentially-Private "Draw and Discard" Machine Learning

no code implementations11 Jul 2018 Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng

In this work, we propose a novel framework for privacy-preserving client-distributed machine learning.

Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12

no code implementations8 Sep 2017 Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, Xiao-Feng Wang

We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers.

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

no code implementations2 May 2017 Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, Benjamin Livshits

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively.

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

2 code implementations25 Jul 2014 Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees.

Cryptography and Security

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