# Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms

arXiv: https://arxiv.org/abs/2009.01098 · HTML: https://mlsanity.com/paper/2009.01098/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Communication efficient privacy-preserving distributed optimization using adaptive differential quantization](https://mlsanity.com/paper/2105.14416/) (2021)
- [Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization](https://mlsanity.com/paper/2407.09324/) (2024)
- [Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation](https://mlsanity.com/paper/2209.07833/) (2022)
- [Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience](https://mlsanity.com/paper/2508.00596/) (2025)
- [Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation](https://mlsanity.com/paper/2403.14905/) (2024)
- [Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization](https://mlsanity.com/paper/2405.15081/) (2024)
- [Secure PAC Bayesian Regression via Real Shamir Secret Sharing](https://mlsanity.com/paper/2109.11200/) (2021)
- [PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds](https://mlsanity.com/paper/2405.15398/) (2024)
- [AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective](https://mlsanity.com/paper/2409.04275/) (2024)

## Abstract (arXiv)

Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation. However, how these algorithms relate to each other is not fully explored yet. In this paper, we therefore first propose information-theoretic metrics based on mutual information. Using the proposed metrics, we are able to compare and relate a number of existing well-known algorithms. We then derive a lower bound on individual privacy that gives insights on the nature of the problem. To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of different aspects such as output utility, individual privacy and algorithm robustness against the number of corrupted parties, using not only theoretical analysis but also numerical validation. Finally, we discuss and provide principles for designing appropriate algorithms for different applications.