# Secure Multi-Function Computation with Private Remote Sources

arXiv: https://arxiv.org/abs/2106.09485 · HTML: https://mlsanity.com/paper/2106.09485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Network Function Virtualization: State-of-the-art and Research Challenges](https://mlsanity.com/paper/1509.07675/) (2015)
- [Privacy, Secrecy, and Storage with Multiple Noisy Measurements of Identifiers](https://mlsanity.com/paper/1601.06756/) (2016)
- [Multi-Entity and Multi-Enrollment Key Agreement with Correlated Noise](https://mlsanity.com/paper/2005.08210/) (2020)
- [Code Constructions for Physical Unclonable Functions and Biometric Secrecy Systems](https://mlsanity.com/paper/1709.00275/) (2017)
- [Private Authentication with Physical Identifiers Through Broadcast Channel Measurements](https://mlsanity.com/paper/1907.01081/) (2019)
- [Biometric and Physical Identifiers with Correlated Noise for Controllable Private Authentication](https://mlsanity.com/paper/2001.00847/) (2020)
- [Converses for Secret Key Agreement and Secure Computing](https://mlsanity.com/paper/1404.5715/) (2014)
- [Controllable Identifier Measurements for Private Authentication with Secret Keys](https://mlsanity.com/paper/1804.01430/) (2018)
- [Secret Key Agreement with Physical Unclonable Functions: An Optimality Summary](https://mlsanity.com/paper/2012.08924/) (2020)

## Built on by (most cited first, 2 of 2)

- [Secure and Private Source Coding with Private Key and Decoder Side Information](https://mlsanity.com/paper/2205.05068/) (2022)
- [Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism](https://mlsanity.com/paper/2604.20245/) (2026)

## Abstract (arXiv)

We consider a distributed function computation problem in which parties observing noisy versions of a remote source facilitate the computation of a function of their observations at a fusion center through public communication. The distributed function computation is subject to constraints, including not only reliability and storage but also privacy and secrecy. Specifically, 1) the remote source should remain private from an eavesdropper and the fusion center, measured in terms of the information leaked about the remote source; 2) the function computed should remain secret from the eavesdropper, measured in terms of the information leaked about the arguments of the function, to ensure secrecy regardless of the exact function used. We derive the exact rate regions for lossless and lossy single-function computation and illustrate the lossy single-function computation rate region for an information bottleneck example, in which the optimal auxiliary random variables are characterized for binary-input symmetric-output channels. We extend the approach to lossless and lossy asynchronous multiple-function computations with joint secrecy and privacy constraints, in which case inner and outer bounds for the rate regions differing only in the Markov chain conditions imposed are characterized.