privacy_preserving_inference.rs

use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
use rand::Rng;
use std::sync::Arc;
use tch::{nn, Device, Tensor};

// Define the audio generation model
struct AudioGenerationModel {
    model: nn::Sequential,
}

impl AudioGenerationModel {
    // Constructor to create a new audio generation model
    fn new(input_size: i64, hidden_size: i64, output_size: i64) -> Self {
        let vs = nn::VarStore::new(Device::Cpu);
        let model = nn::seq()
            .add(nn::linear(&vs.root(), input_size, hidden_size, Default::default()))
            .add_fn(|xs| xs.relu())
            .add(nn::linear(&vs.root(), hidden_size, output_size, Default::default()));
        AudioGenerationModel { model }
    }

    // Forward method to perform inference with the model
    fn forward(&self, input: &Tensor) -> Tensor {
        self.model.forward(input)
    }
}

// Define the differential privacy mechanism
struct DifferentialPrivacy {
    epsilon: f64,
    sensitivity: f64,
}

impl DifferentialPrivacy {
    // Constructor to create a new differential privacy mechanism
    fn new(epsilon: f64, sensitivity: f64) -> Self {
        DifferentialPrivacy { epsilon, sensitivity }
    }

    // Method to add noise to the data for differential privacy
    fn add_noise(&self, data: &Tensor) -> Tensor {
        let scale = self.sensitivity / self.epsilon;
        let noise = Tensor::rand(data.size(), data.kind()) * scale;
        data + noise
    }
}

// Function to perform privacy-preserving inference
fn privacy_preserving_inference(
    model: &AudioGenerationModel,
    data: &[f32],
    epsilon: f64,
    sensitivity: f64,
) -> Vec<f32> {
    // Convert the input data to a tensor
    let input_tensor = Tensor::of_slice(data).reshape(&[1, data.len() as i64]);

    // Create the differential privacy mechanism
    let dp = DifferentialPrivacy::new(epsilon, sensitivity);

    // Perform a forward pass through the model
    let mut output_tensor = model.forward(&input_tensor);

    // Add differential privacy noise to the output
    output_tensor = dp.add_noise(&output_tensor);

    // Convert the output tensor to a vector
    output_tensor.data().as_slice().unwrap().to_vec()
}

// Python module to expose privacy-preserving inference functions
#[pymodule]
fn privacy_preserving_inference(_py: Python, m: &PyModule) -> PyResult<()> {
    // Function to perform privacy-preserving inference
    #[pyfn(m)]
    #[pyo3(name = "perform_inference")]
    fn perform_inference_py(
        model_path: &str,
        input_size: i64,
        hidden_size: i64,
        output_size: i64,
        data: Vec<f32>,
        epsilon: f64,
        sensitivity: f64,
    ) -> PyResult<Vec<f32>> {
        // Load the trained audio generation model
        // Possible Error: Model loading might fail due to an incorrect path or file format.
        // Solution: Ensure the model path is correct and the file is a valid PyTorch model.
        let model = tch::CModule::load(model_path)
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyIOError, _>(format!("Failed to load model: {}", e)))?;

        // Initialize the audio generation model
        let model = AudioGenerationModel {
            model: model.sequential(),
        };

        // Perform privacy-preserving inference
        // Possible Error: Inference might fail due to incompatible input data.
        // Solution: Ensure the input data matches the expected shape and type.
        let output = privacy_preserving_inference(&model, &data, epsilon, sensitivity);

        Ok(output)
    }

    Ok(())
}