# Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databases

arXiv: https://arxiv.org/abs/2104.03354 · HTML: https://mlsanity.com/paper/2104.03354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 64 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Conclave: secure multi-party computation on big data (extended TR)](https://mlsanity.com/paper/1902.06288/) (2019)
- [SMCQL: Secure Querying for Federated Databases](https://mlsanity.com/paper/1606.06808/) (2016)

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

- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [Balancing Privacy and Efficiency: Music Information Retrieval via Additive Homomorphic Encryption](https://mlsanity.com/paper/2508.07044/) (2025)
- [Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval](https://mlsanity.com/paper/2604.09541/) (2026)

## Abstract (arXiv)

This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.