PSI.md

PSI

Introduction

In federated learning, a secure and dependable method is required to find any intersection in features or IDs between datasets from two parties. The intersection data will then be used to update the model. We focus on the setting where two parties (e.g., A & B) hold vertically partitioned data. They need to align their data before training the model.

Private Set Intersection (PSI) protocol is well-suited to solve this problem. Our implementation relies on the elliptic curve Diffie-Hellman PSI (ECDH-PSI) provided by PETAce. (More detailed information is available in the PETAce documentation.) To improve user experience, we encapsulated the PSI function in PETAce, presenting it as an intersection module. Users can simply upload a CSV file along with the names of the columns to be aligned, enabling them to obtain their aligned data effortlessly.

Config

Module Name

petml.operators.preprocessing.PSITransform

Model Parameters

Name	Type	Description	Default
column_name	str	The column used to implement PSI

Input

Name	File Type	Description
data	csv	The data for PSI

Output

Name	File Type	Description
intersection	csv	The intersection of data

Examples

config = {
    "common": {
        "network_mode": "petnet",
        "network_scheme": "socket",
        "parties": {
            "party_a": {
                "address": ["127.0.0.1:50011"]
            },
            "party_b": {
                "address": ["127.0.0.1:50012"]
            }
        }
    },
    "party_a": {
        "column_name": "id",
        "inputs": {
            "data": "examples/data/breast_hetero_mini_server.csv",
        },
        "outputs": {
            "data": "tmp/server/data.csv"
        }
    },
    "party_b": {
        "column_name": "id",
        "inputs": {
            "data": "examples/data/breast_hetero_mini_client.csv",
        },
        "outputs": {
            "data": "tmp/client/data.csv"
        }
    }
}

#if run the code in party a, the party should be 'party_a' and vice versa
operator = petml.operators.preprocessing.PSITransform(party)
operator.run(config_map)