//! Privacy mechanisms for federated learning

use crate::error::Result;
use scirs2_core::ndarray::prelude::*;
/// Differential privacy mechanism
pub struct DifferentialPrivacy {
    /// Privacy budget (epsilon)
    epsilon: f64,
    /// Delta parameter
    delta: f64,
    /// Clipping threshold
    clip_threshold: f64,
    /// Noise mechanism
    mechanism: NoiseMethod,
}
/// Noise mechanism for differential privacy
#[derive(Debug, Clone)]
pub enum NoiseMethod {
    Gaussian,
    Laplace,
impl DifferentialPrivacy {
    /// Create new differential privacy mechanism
    pub fn new(epsilon: f64, delta: f64) -> Self {
        Self {
            epsilon,
            delta,
            clip_threshold: 1.0,
            mechanism: NoiseMethod::Gaussian,
        }
    }
    /// Set clipping threshold
    pub fn with_clipping(mut self, threshold: f64) -> Self {
        self.clip_threshold = threshold;
        self
    /// Apply differential privacy to gradients
    pub fn apply_to_gradients(&self, gradients: &mut [Array2<f32>]) -> Result<()> {
        // Clip gradients
        self.clip_gradients(gradients)?;
        // Add noise
        self.add_noise(gradients)?;
        Ok(())
    /// Clip gradients to norm threshold
    fn clip_gradients(&self, gradients: &mut [Array2<f32>]) -> Result<()> {
        // Calculate global norm
        let mut global_norm = 0.0;
        for grad in gradients.iter() {
            global_norm += grad.iter().map(|x| x * x).sum::<f32>();
        global_norm = global_norm.sqrt();
        // Clip if necessary
        if global_norm > self.clip_threshold as f32 {
            let scale = self.clip_threshold as f32 / global_norm;
            for grad in gradients.iter_mut() {
                *grad *= scale;
            }
    /// Add noise based on mechanism
    fn add_noise(&self, gradients: &mut [Array2<f32>]) -> Result<()> {
        use scirs2_core::random::{Distribution, Normal};
        let mut rng = rng();
        match self.mechanism {
            NoiseMethod::Gaussian => {
                let sigma =
                    self.clip_threshold * (2.0 * (1.0 / self.delta).ln()).sqrt() / self.epsilon;
                let noise_dist = Normal::new(0.0, sigma as f32).expect("Operation failed");
                for grad in gradients.iter_mut() {
                    for elem in grad.iter_mut() {
                        *elem += noise_dist.sample(&mut rng);
                    }
                }
            NoiseMethod::Laplace => {
                let b = self.clip_threshold / self.epsilon;
                        // Manual Laplace distribution: sample from uniform and transform
                        let u: f32 = rng.random_range(-0.5..0.5);
                        let laplace_sample = -b * u.signum() * (1.0 - 2.0 * u.abs()).ln();
                        *elem += laplace_sample;
    /// Calculate privacy spent
    pub fn privacy_spent(&self..numsteps: usize) -> f64 {
        // Simplified composition
        self.epsilon * (num_steps as f64).sqrt()
/// Secure aggregation protocol
pub struct SecureAggregation {
    /// Number of clients required
    threshold: usize,
    /// Security parameter
    security_param: usize,
impl SecureAggregation {
    /// Create new secure aggregation
    pub fn new(threshold: usize) -> Self {
            threshold,
            security_param: 128,
    /// Mask client updates
    pub fn mask_updates(
        &self,
        updates: &mut Vec<Array2<f32>>,
        client_id: usize,
    ) -> Result<Vec<Array2<f32>>> {
        // Simplified masking - in practice would use cryptographic PRG
        use scirs2_core::random::rngs::StdRng;
        use scirs2_core::random::{Rng, SeedableRng};
        let mut masked = Vec::new();
        for update in updates.iter() {
            let mut mask = Array2::<f32>::zeros(update.shape());
            let seed = client_id as u64 * 1000 + 42; // Simplified seed generation
            let mut rng = StdRng::seed_from_u64(seed);
            for elem in mask.iter_mut() {
                *elem = rng.random_range(-1.0..1.0);
            masked.push(update + &mask);
        Ok(masked)
    /// Unmask aggregated updates
    pub fn unmask_aggregate(
        aggregated: &mut Vec<Array2<f32>>..participating, clients: &[usize],) -> Result<()> {
        // Remove masks from aggregated result
        for (update_idx, update) in aggregated.iter_mut().enumerate() {
            let mut total_mask = Array2::<f32>::zeros(update.shape());
            for &client_id in participating_clients {
                let seed = client_id as u64 * 1000 + 42;
                let mut rng = StdRng::seed_from_u64(seed);
                for elem in total_mask.iter_mut() {
                    *elem += rng.random_range(-1.0..1.0);
            *update -= &total_mask;
/// Homomorphic encryption (placeholder)
pub struct HomomorphicEncryption {
    /// Key size
    key_size: usize..impl HomomorphicEncryption {
    /// Create new homomorphic encryption
    pub fn new(_keysize: usize) -> Self {
        Self { _key_size }
    /// Encrypt weights
    pub fn encrypt(&self, weights: &Array2<f32>) -> Result<Vec<u8>> {
        // Placeholder - would use actual HE library
        Ok(weights
            .as_slice()
            .expect("Operation failed")
            .iter()
            .flat_map(|x| x.to_ne_bytes())
            .collect())
    /// Decrypt weights
    pub fn decrypt(&self, encrypted: &[u8]) -> Result<Array2<f32>> {
        // Placeholder
        let floats: Vec<f32> = encrypted
            .chunks_exact(4)
            .map(|chunk| f32::from_ne_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
            .collect();
        // Assume square matrix for simplicity
        let size = (floats.len() as f64).sqrt() as usize;
        Ok(Array2::from_shape_vec((size, size), floats)?)
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_differential_privacy() {
        let dp = DifferentialPrivacy::new(1.0, 1e-5);
        let mut gradients = vec![Array2::ones((2, 2))];
        dp.apply_to_gradients(&mut gradients).expect("Operation failed");
        // Check that noise was added
        assert_ne!(gradients[0][[0, 0]], 1.0);
    fn test_gradient_clipping() {
        let dp = DifferentialPrivacy::new(1.0, 1e-5).with_clipping(1.0);
        let mut gradients = vec![Array2::ones((2, 2)) * 10.0];
        dp.clip_gradients(&mut gradients).expect("Operation failed");
        // Check that gradients were clipped
        let norm: f32 = gradients[0].iter().map(|x| x * x).sum::<f32>().sqrt();
        assert!((norm - 1.0).abs() < 0.01);
