ai_dht_paper.md

Purpose of DHT in Self-Sovereign AI

Author: Rsync25

1. Data Discovery: The DHT allows AI agents to locate and retrieve data across a distributed network without centralized control.
2. Decentralized Storage: Patient data can be distributed across hospital nodes securely, ensuring data redundancy and availability without a central server.
3. Efficient Communication: AI agents can identify which nodes hold specific datasets, reducing the need for extensive queries.

Rust Code Example with DHT

We'll implement a basic DHT setup for decentralized storage using libp2p, which supports DHT and P2P communication. This code focuses on setting up nodes that communicate and share data in a healthcare context.

Step 1: Setting Up Dependencies

Add the following dependencies in Cargo.toml:

[dependencies]
libp2p = { version = "0.42", features = ["tcp-tokio", "mdns"] }
tokio = { version = "1.25", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"

Step 2: Define the DHT Node for Hospital Data Exchange

The following Rust code initializes a P2P DHT node that can store and retrieve data, representing a hospital’s node in the DHT. The hospital AI agent will store patient data securely and allow retrieval by authorized agents.

use libp2p::{
    identity, mdns::Mdns, swarm::SwarmBuilder, kad::{Kademlia, KademliaConfig, store::MemoryStore, Quorum, GetProvidersOk, PutRecordOk, record::Record},
    PeerId, Multiaddr, Swarm, NetworkBehaviour
};
use tokio::io::{self, AsyncBufReadExt};
use std::error::Error;

#[derive(NetworkBehaviour)]
struct HospitalNetwork {
    kademlia: Kademlia<MemoryStore>,
    mdns: Mdns,
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
    // Create identity for the hospital node
    let local_key = identity::Keypair::generate_ed25519();
    let local_peer_id = PeerId::from(local_key.public());
    println!("Hospital Node Peer ID: {:?}", local_peer_id);

    // Set up DHT and mDNS for local peer discovery
    let store = MemoryStore::new(local_peer_id.clone());
    let mut kademlia = Kademlia::with_config(local_peer_id.clone(), store, KademliaConfig::default());
    let mdns = Mdns::new()?;

    let mut swarm = SwarmBuilder::new(HospitalNetwork { kademlia, mdns }, local_peer_id.clone(), tokio::spawn).build();

    // Event loop for DHT commands
    let stdin = io::BufReader::new(io::stdin()).lines();
    tokio::pin!(stdin);

    println!("Enter 'store <key> <value>' to add patient data, 'find <key>' to retrieve.");

    loop {
        tokio::select! {
            line = stdin.next_line() => match line?.as_deref() {
                Some(line) if line.starts_with("store") => {
                    let parts: Vec<&str> = line.split_whitespace().collect();
                    if parts.len() == 3 {
                        let key = parts[1].to_string();
                        let value = parts[2].to_string().into_bytes();
                        let record = Record { key: key.into_bytes(), value };
                        swarm.behaviour_mut().kademlia.put_record(record, Quorum::One)?;
                        println!("Storing patient record under key: {}", parts[1]);
                    }
                }
                Some(line) if line.starts_with("find") => {
                    let key = line.split_whitespace().nth(1).expect("Key missing").to_string();
                    swarm.behaviour_mut().kademlia.get_providers(key.into_bytes());
                }
                _ => {}
            },
            event = swarm.select_next_some() => {
                if let libp2p::kad::KademliaEvent::PutRecordResult(Ok(PutRecordOk { key })) = event {
                    println!("Successfully stored data for key: {:?}", String::from_utf8_lossy(&key));
                } else if let libp2p::kad::KademliaEvent::GetProvidersResult(Ok(GetProvidersOk { key, providers, .. })) = event {
                    for provider in providers {
                        println!("Provider found for key {:?}: {:?}", String::from_utf8_lossy(&key), provider);
                    }
                }
            }
        }
    }
}

Explanation

1. Initialization: The code creates a new DHT node (HospitalNetwork) using libp2p. This node can participate in a Kademlia DHT, allowing it to store and retrieve records associated with specific keys.
2. Storing Data: The store command is used to add patient data, e.g., store patient123 {"name": "John Doe", "age": 45, "diagnosis": "Hypertension"}. This data is hashed and added to the DHT as a record.
3. Finding Data: The find command looks for providers of the specified key, which could represent another hospital or authorized entity in the network with access to the data.

Example: Hospital Data Sharing

In a decentralized hospital network, each node could represent a hospital that:

• Stores patient records securely in the DHT.
• Uses peer-to-peer communication for data requests and retrieval.
• Ensures privacy by encrypting patient data before storage and only sharing decryption keys with authorized nodes.

Hospital Network Scenario

1. Patient Admission: Hospital A admits a patient, stores the encrypted record in the DHT, and makes it accessible by storing only references (hashes) to the data in the DHT.
2. Treatment and Consultation: Hospital B, treating the patient, requests access to the patient’s records by querying the DHT. Hospital A’s node provides the necessary data, allowing Hospital B to decrypt and review it.
3. Federated Learning for Health Insights: All hospitals can participate in a federated learning model where local models are trained on encrypted data. These models are aggregated without data exposure, enhancing healthcare insights while preserving privacy.

Extending the Example

This setup can be expanded by:

• Adding Privacy-Preserving Computation: Using techniques like homomorphic encryption to ensure computations can be done on encrypted data without revealing patient details.
• Integration with Identity: Implementing DIDs or compatible with Nostr, Pubky to verify the identity of each hospital node in the network, ensuring only verified entities access the DHT.
• Federated Learning: Integrate a federated learning pipeline where each hospital trains local models on patient data, sharing only model updates to improve a global model without exposing raw data.

This framework offers a foundation for a decentralized healthcare application that can maintain patient privacy, improve data availability across hospitals, and enable collaborative insights.