Privacy amplification via shuffling: Unified, simplified, and tightened
S Wang - arXiv preprint arXiv:2304.05007, 2023 - arxiv.org
In decentralized settings, the shuffle model of differential privacy has emerged as a
promising alternative to the classical local model. Analyzing privacy amplification via …
promising alternative to the classical local model. Analyzing privacy amplification via …
Renyi differential privacy in the shuffle model: Enhanced amplification bounds
E Chen, Y Cao, Y Ge - ICASSP 2024-2024 IEEE International …, 2024 - ieeexplore.ieee.org
The shuffle model of Differential Privacy (DP) has gained significant attention in privacy-
preserving data analysis due to its remarkable tradeoff between privacy and utility. It is …
preserving data analysis due to its remarkable tradeoff between privacy and utility. It is …
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
E Chen, Y Cao, Y Ge - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
The shuffle model of local differential privacy is an advanced method of privacy amplification
designed to enhance privacy protection with high utility. It achieves this by randomly …
designed to enhance privacy protection with high utility. It achieves this by randomly …
On distributed differential privacy and counting distinct elements
We study the setup where each of $ n $ users holds an element from a discrete set, and the
goal is to count the number of distinct elements across all users, under the constraint of …
goal is to count the number of distinct elements across all users, under the constraint of …
Tight accounting in the shuffle model of differential privacy
Shuffle model of differential privacy is a novel distributed privacy model based on a
combination of local privacy mechanisms and a secure shuffler. It has been shown that the …
combination of local privacy mechanisms and a secure shuffler. It has been shown that the …
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …
implement differentially private mechanisms over distributed data with utility matching the …
Differentially private histograms in the shuffle model from fake users
A Cheu, M Zhilyaev - 2022 IEEE Symposium on Security and …, 2022 - ieeexplore.ieee.org
There has been much recent work in the shuffle model of differential privacy, particularly for
approximate d-bin histograms. While these protocols achieve low error, the number of …
approximate d-bin histograms. While these protocols achieve low error, the number of …
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta 1
demonstrates that random shuffling amplifies differential privacy guarantees of locally …
demonstrates that random shuffling amplifies differential privacy guarantees of locally …
Privacy enhancement via dummy points in the shuffle model
The shuffle model is recently proposed to address the issue of severe utility loss in Local
Differential Privacy (LDP) due to distributed data randomization. In the shuffle model, a …
Differential Privacy (LDP) due to distributed data randomization. In the shuffle model, a …
Separating local & shuffled differential privacy via histograms
V Balcer, A Cheu - arXiv preprint arXiv:1911.06879, 2019 - arxiv.org
Recent work in differential privacy has highlighted the shuffled model as a promising avenue
to compute accurate statistics while keeping raw data in users' hands. We present a protocol …
to compute accurate statistics while keeping raw data in users' hands. We present a protocol …