# MPC Validation and Aggregation of Unit Vectors

## TL;DR

This paper presents a secure MPC method for validating and aggregating unit vectors, enabling privacy-preserving verification and aggregation of categorical data in distributed systems.

## Contribution

It introduces a novel MPC technique for verifying blinded unit vectors and a BGW circuit for secure aggregation with threshold release, enhancing privacy in categorical data processing.

## Key findings

- Successfully verifies blinded unit vectors in MPC setting
- Securely aggregates inputs with threshold-based output release
- Applicable to systems using one-hot encoding or categorical choices

## Abstract

When dealing with privatized data, it is important to be able to protect against malformed user inputs. This becomes difficult in MPC systems as each server should not contain enough information to know what values any user has submitted. In this paper, we implement an MPC technique to verify blinded user inputs are unit vectors. In addition, we introduce a BGW circuit which can securely aggregate the blinded inputs while only releasing the result when it is above a public threshold. These distributed techniques take as input a unit vector. While this initially seems limiting compared to real number input, it is quite powerful for cases such as selecting from a list of options, indicating a location from a set of possibilities, or any system which uses one-hot encoding.