# 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} }

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…

## 6 Citations

### Improved Utility Analysis of Private CountSketch

- Computer ScienceArXiv
- 2022

This paper considers the classical CountSketch, made differentially private with the Gaussian mechanism, and gives an improved analysis of its estimation error, finding the privacy-utility trade-off is essentially the best one could hope for.

### Frequency Estimation in the Shuffle Model with Almost a Single Message

- Computer ScienceCCS
- 2022

By combining the frequency estimation and the heavy hitter detection protocols, this work shows how to solve the B-dimensional 1-sparse vector summation problem in the high-dimensional setting B=Ω(n), achieving the optimal central-DP MSE Õ(n) with 1 + o(1) messages per user.

### Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data

- Computer Science, MathematicsPODS
- 2022

The key breakthrough is a new randomizer for sequential data, FutureRand, with two key features: a composition strategy that correlates the noise across the non-zero elements of the sequence, and a pre-computation technique which enables the randomizer to output the results on the fly, without knowing future inputs.

### Network change point localisation under local differential privacy

- Computer Science
- 2022

This paper investigates the fundamental limits in consistently localising change points under both node and edge privacy constraints, demon-strating interesting phase transition in terms of the signal-to-noise ratio condition, accompanied by polynomial-time algorithms.

### MinMax Sampling: A Near-optimal Global Summary for Aggregation in the Wide Area

- Computer ScienceSIGMOD Conference
- 2022

This paper proposes MinMax Sampling, a fast, adaptive, and accurate communication scheme for global aggregation in WAN, and designs a scheme, namely MinMaxopt, which trades little accuracy for the other two requirements.

### Huff-DP: Huffman Coding based Differential Privacy Mechanism for Real-Time Data

- Computer Science
- 2023

A novel Huffman coding based differential privacy budget selection mechanism (Huff-DP), which selects the optimal privacy budget on the basis of privacy requirement for that speciﬁc record, and proposes static, sine, and fuzzy logic based decision algorithms.

## References

SHOWING 1-10 OF 58 REFERENCES

### Local, Private, Efficient Protocols for Succinct Histograms

- Computer Science, MathematicsSTOC
- 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.

### Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access

- Computer ScienceCCS
- 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.

### Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication

- Computer ScienceAISTATS
- 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.

### Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

- Computer ScienceCCS
- 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.

### Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

- Computer ScienceICML
- 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.

### Practical Secure Aggregation for Privacy-Preserving Machine Learning

- Computer ScienceIACR 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.

### Prio: Private, Robust, and Scalable Computation of Aggregate Statistics

- Computer Science, MathematicsNSDI
- 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.

### Locally Differentially Private Protocols for Frequency Estimation

- Computer ScienceUSENIX 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.

### Differentially private summaries for sparse data

- Computer ScienceICDT '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.

### Heavy Hitters and the Structure of Local Privacy

- Computer Science, MathematicsPODS
- 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…