# UN Handbook on Privacy-Preserving Computation Techniques

arXiv: https://arxiv.org/abs/2301.06167 · HTML: https://mlsanity.com/paper/2301.06167/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [Cooperative AI via Decentralized Commitment Devices](https://mlsanity.com/paper/2311.07815/) (2023)
- [QualitEye: Public and Privacy-preserving Gaze Data Quality Verification](https://mlsanity.com/paper/2506.05908/) (2025)
- [Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation](https://mlsanity.com/paper/2609.01945/) (2026)

## Abstract (arXiv)

This paper describes privacy-preserving approaches for the statistical analysis. It describes motivations for privacy-preserving approaches for the statistical analysis of sensitive data, presents examples of use cases where such methods may apply and describes relevant technical capabilities to assure privacy preservation while still allowing analysis of sensitive data. Our focus is on methods that enable protecting privacy of data while it is being processed, not only while it is at rest on a system or in transit between systems. The information in this document is intended for use by statisticians and data scientists, data curators and architects, IT specialists, and security and information assurance specialists, so we explicitly avoid cryptographic technical details of the technologies we describe.