MPC Validation and Aggregation of Unit Vectors
This addresses the challenge of input validation and aggregation in privacy-preserving systems, particularly for applications like selection or one-hot encoding, but it is incremental as it builds on existing MPC and BGW methods.
The paper tackles the problem of protecting against malformed inputs in MPC systems by implementing a technique to verify that blinded user inputs are unit vectors and introducing a BGW circuit to securely aggregate them, releasing results only above a public threshold.
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.