# Locally Differentially Private Sparse Vector Aggregation

@article{Zhou2021LocallyDP,
title={Locally Differentially Private Sparse Vector Aggregation},
author={Mingxun Zhou and Tianhao Wang and T-H. Hubert Chan and Giulia C. Fanti and Elaine Shi},
journal={2022 IEEE Symposium on Security and Privacy (SP)},
year={2021},
pages={422-439}
}
• Published 7 December 2021
• Computer Science
• 2022 IEEE Symposium on Security and Privacy (SP)
Vector mean estimation is a central primitive in federated analytics. In vector mean estimation, each user $i \in[n]$ holds a real-valued vector $v_{i} \in[-1,1]^{d}$, and a server wants to estimate the mean of all n vectors; we would additionally like to protect each user’s privacy. In this paper, we consider the k-sparse version of the vector mean estimation problem. That is, suppose each user’s vector has at most k non-zero coordinates in its d-dimensional vector, and moreover, $k \ll d$. In…

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## References

SHOWING 1-10 OF 58 REFERENCES

• Computer Science, Mathematics
STOC
• 2015
Efficient protocols and matching accuracy lower bounds for frequency estimation in the local model for differential privacy are given and it is shown that each user need only send 1 bit to the server in a model with public coins.
• Computer Science
CCS
• 2021
The Approximate Laplace Projection (ALP) mechanism for approximating k-sparse vectors is shown to simultaneously have information-theoretically optimal space, fast access to vector entries, and error of the same magnitude as the Laplace-mechanism applied to dense vectors.
• Computer Science
AISTATS
• 2019
Hadamard Response (HR) is proposed, a local privatization scheme that requires no shared randomness and is symmetric with respect to the users, and which runs about 100x faster than Randomized Response, RAPPOR, and subset-selection mechanisms.
• Computer Science
CCS
• 2016
The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget-wise than obtaining the heavy hitters directly from the whole dataset.
• Computer Science
ICML
• 2019
This work proposes a sample-optimal $\varepsilon$-locally differentially private (LDP) scheme for distribution estimation, where each user communicates only one bit, and requires no public randomness.
• Computer Science
IACR Cryptol. ePrint Arch.
• 2017
This protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner, and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network.
• Computer Science, Mathematics
NSDI
• 2017
Pozo is presented, a privacy-preserving system for the collection of aggregate statistics that uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches.
• Computer Science
USENIX Security Symposium
• 2017
This paper introduces a framework that generalizes several LDP protocols proposed in the literature and yields a simple and fast aggregation algorithm, whose accuracy can be precisely analyzed, resulting in two new protocols that provide better utility than protocols previously proposed.
• Computer Science
ICDT '12
• 2012
This work proposes a general framework for computing the summary directly from the input data, without materializing the vast noisy data, and shows that this is a highly practical solution, which releases a compact summary of the noisy data.
• Computer Science, Mathematics
PODS
• 2018
We present a new locally differentially private algorithm for the heavy hitters problem which achieves optimal worst-case error as a function of all standardly considered parameters. Prior work