trustformers-mobile 0.1.1

//! Privacy configuration and differential privacy mechanisms
//!
//! This module implements advanced differential privacy mechanisms, composition methods,
//! noise generation, and privacy amplification techniques for federated learning.

use serde::{Deserialize, Serialize};
use crate::federated_learning_v2_backup::types::*;
use trustformers_core::{Result, Tensor};

/// Advanced differential privacy configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedPrivacyConfig {
    /// Privacy budget (epsilon)
    pub epsilon: f64,
    /// Privacy budget (delta)
    pub delta: f64,
    /// Privacy mechanism type
    pub mechanism: PrivacyMechanism,
    /// Local vs central differential privacy
    pub privacy_model: PrivacyModel,
    /// Advanced composition method
    pub composition_method: CompositionMethod,
    /// Adaptive privacy budgeting
    pub adaptive_budgeting: bool,
    /// Privacy amplification settings
    pub amplification_config: PrivacyAmplificationConfig,
    /// Noise distribution parameters
    pub noise_config: NoiseDistributionConfig,
}

/// Privacy amplification configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrivacyAmplificationConfig {
    /// Subsampling ratio
    pub subsampling_ratio: f64,
    /// Shuffling enabled
    pub shuffling_enabled: bool,
    /// Secure aggregation amplification
    pub secure_aggregation_amplification: bool,
    /// Random sampling method
    pub sampling_method: SamplingMethod,
}

/// Noise distribution configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseDistributionConfig {
    /// Base noise multiplier
    pub noise_multiplier: f64,
    /// Clipping norm for gradient clipping
    pub clipping_norm: f64,
    /// Adaptive clipping enabled
    pub adaptive_clipping: bool,
    /// Per-layer noise scaling
    pub per_layer_scaling: bool,
    /// Correlated noise for efficiency
    pub correlated_noise: bool,
}

/// Privacy accounting configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrivacyAccountingConfig {
    /// Accounting method
    pub accounting_method: PrivacyAccountingMethod,
    /// Target epsilon for optimization
    pub target_epsilon: f64,
    /// Target delta for optimization
    pub target_delta: f64,
    /// Maximum number of rounds
    pub max_rounds: u32,
    /// Privacy loss tracking enabled
    pub privacy_loss_tracking: bool,
    /// Composition analysis enabled
    pub composition_analysis: bool,
}

/// Privacy accountant for tracking privacy budget consumption
#[derive(Debug, Clone)]
pub struct PrivacyAccountant {
    /// Current privacy configuration
    privacy_config: AdvancedPrivacyConfig,
    /// Accounting configuration
    accounting_config: PrivacyAccountingConfig,
    /// Total epsilon consumed
    total_epsilon: f64,
    /// Total delta consumed
    total_delta: f64,
    /// Per-round privacy consumption
    round_privacy_consumption: Vec<(f64, f64)>, // (epsilon, delta) per round
}

impl PrivacyAccountant {
    /// Create a new privacy accountant
    pub fn new(
        privacy_config: &AdvancedPrivacyConfig,
        accounting_config: &PrivacyAccountingConfig,
    ) -> Result<Self> {
        Ok(Self {
            privacy_config: privacy_config.clone(),
            accounting_config: accounting_config.clone(),
            total_epsilon: 0.0,
            total_delta: 0.0,
            round_privacy_consumption: Vec::new(),
        })
    }

    /// Account for privacy budget consumption in a round
    pub fn account_round(&mut self, epsilon: f64, delta: f64) -> Result<()> {
        let (composed_epsilon, composed_delta) = match self.privacy_config.composition_method {
            CompositionMethod::Basic => {
                (self.total_epsilon + epsilon, self.total_delta + delta)
            }
            CompositionMethod::Advanced => {
                // Advanced composition with tighter bounds
                let k = self.round_privacy_consumption.len() as f64 + 1.0;
                let composed_eps = epsilon * (2.0 * k * (k * epsilon).ln()).sqrt();
                (self.total_epsilon + composed_eps, self.total_delta + k * delta)
            }
            CompositionMethod::Optimal => {
                // Optimal composition (simplified)
                let k = self.round_privacy_consumption.len() as f64 + 1.0;
                let composed_eps = epsilon * (k * (k * epsilon).ln()).sqrt();
                (self.total_epsilon + composed_eps, self.total_delta + k * delta)
            }
            CompositionMethod::RenyiComposition => {
                // Renyi DP composition (simplified)
                (self.total_epsilon + epsilon, self.total_delta + delta)
            }
            CompositionMethod::ZCDPComposition => {
                // Zero-concentrated DP composition (simplified)
                (self.total_epsilon + epsilon, self.total_delta + delta)
            }
            CompositionMethod::PLDTracking => {
                // Privacy loss distribution tracking (simplified)
                (self.total_epsilon + epsilon, self.total_delta + delta)
            }
        };

        self.total_epsilon = composed_epsilon;
        self.total_delta = composed_delta;
        self.round_privacy_consumption.push((epsilon, delta));

        Ok(())
    }

    /// Get current privacy budget consumption
    pub fn get_privacy_budget(&self) -> (f64, f64) {
        (self.total_epsilon, self.total_delta)
    }

    /// Check if privacy budget is exhausted
    pub fn is_budget_exhausted(&self) -> bool {
        self.total_epsilon >= self.accounting_config.target_epsilon
            || self.total_delta >= self.accounting_config.target_delta
    }

    /// Get remaining privacy budget
    pub fn get_remaining_budget(&self) -> (f64, f64) {
        (
            (self.accounting_config.target_epsilon - self.total_epsilon).max(0.0),
            (self.accounting_config.target_delta - self.total_delta).max(0.0),
        )
    }

    /// Optimize privacy parameters for remaining rounds
    pub fn optimize_privacy_parameters(&self, remaining_rounds: u32) -> (f64, f64) {
        let (remaining_epsilon, remaining_delta) = self.get_remaining_budget();

        // Simple equal allocation strategy
        let epsilon_per_round = remaining_epsilon / remaining_rounds as f64;
        let delta_per_round = remaining_delta / remaining_rounds as f64;

        (epsilon_per_round, delta_per_round)
    }
}

/// Differential privacy mechanism implementation
pub struct DifferentialPrivacyMechanism {
    config: AdvancedPrivacyConfig,
}

impl DifferentialPrivacyMechanism {
    /// Create a new differential privacy mechanism
    pub fn new(config: AdvancedPrivacyConfig) -> Self {
        Self { config }
    }

    /// Apply differential privacy to a tensor
    pub fn apply_privacy(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        match self.config.mechanism {
            PrivacyMechanism::Gaussian => self.apply_gaussian_mechanism(tensor, sensitivity),
            PrivacyMechanism::Laplace => self.apply_laplace_mechanism(tensor, sensitivity),
            PrivacyMechanism::Exponential => self.apply_exponential_mechanism(tensor, sensitivity),
            PrivacyMechanism::AboveThreshold => self.apply_above_threshold_mechanism(tensor, sensitivity),
            PrivacyMechanism::SparseVector => self.apply_sparse_vector_technique(tensor, sensitivity),
            PrivacyMechanism::PATE => self.apply_pate_mechanism(tensor, sensitivity),
            PrivacyMechanism::RenyiDP => self.apply_renyi_dp_mechanism(tensor, sensitivity),
            PrivacyMechanism::ZCDP => self.apply_zcdp_mechanism(tensor, sensitivity),
        }
    }

    /// Apply Gaussian mechanism
    fn apply_gaussian_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        let sigma = sensitivity * self.config.noise_config.noise_multiplier;
        let data = tensor.data()?;
        let mut noisy_data = Vec::with_capacity(data.len());

        for &value in data.iter() {
            // Simplified noise generation (in practice, use proper RNG)
            let noise = self.generate_gaussian_noise(0.0, sigma);
            noisy_data.push(value + noise);
        }

        Tensor::from_vec(noisy_data, tensor.shape())
    }

    /// Apply Laplace mechanism
    fn apply_laplace_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        let scale = sensitivity / self.config.epsilon;
        let data = tensor.data()?;
        let mut noisy_data = Vec::with_capacity(data.len());

        for &value in data.iter() {
            let noise = self.generate_laplace_noise(0.0, scale);
            noisy_data.push(value + noise);
        }

        Tensor::from_vec(noisy_data, tensor.shape())
    }

    /// Apply exponential mechanism (simplified)
    fn apply_exponential_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        // Simplified implementation - in practice, this would select from a discrete set
        self.apply_laplace_mechanism(tensor, sensitivity)
    }

    /// Apply above threshold mechanism (simplified)
    fn apply_above_threshold_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        self.apply_laplace_mechanism(tensor, sensitivity)
    }

    /// Apply sparse vector technique (simplified)
    fn apply_sparse_vector_technique(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        self.apply_laplace_mechanism(tensor, sensitivity)
    }

    /// Apply PATE mechanism (simplified)
    fn apply_pate_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        self.apply_gaussian_mechanism(tensor, sensitivity)
    }

    /// Apply Renyi DP mechanism (simplified)
    fn apply_renyi_dp_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        self.apply_gaussian_mechanism(tensor, sensitivity)
    }

    /// Apply zero-concentrated DP mechanism (simplified)
    fn apply_zcdp_mechanism(&self, tensor: &Tensor, sensitivity: f64) -> Result<Tensor> {
        self.apply_gaussian_mechanism(tensor, sensitivity)
    }

    /// Generate Gaussian noise (simplified - use proper cryptographic RNG in practice)
    fn generate_gaussian_noise(&self, mean: f64, std_dev: f64) -> f32 {
        // Simplified Box-Muller transform
        use std::f64::consts::PI;
        static mut U1: Option<f64> = None;
        static mut U2: Option<f64> = None;

        unsafe {
            if U1.is_none() {
                // Generate two uniform random numbers (simplified)
                let u1 = 0.5; // In practice, use proper RNG
                let u2 = 0.5; // In practice, use proper RNG

                let z0 = (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos();
                let z1 = (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).sin();

                U1 = Some(z0);
                U2 = Some(z1);
            }

            let noise = U1.expect("U1 was just set") * std_dev + mean;
            U1 = U2;
            U2 = None;

            noise as f32
        }
    }

    /// Generate Laplace noise (simplified)
    fn generate_laplace_noise(&self, location: f64, scale: f64) -> f32 {
        // Simplified inverse transform sampling
        let u = 0.5; // In practice, use proper RNG: uniform random in (-0.5, 0.5)
        let noise = if u < 0.0 {
            location + scale * (2.0 * (u + 0.5)).ln()
        } else {
            location - scale * (2.0 * (0.5 - u)).ln()
        };
        noise as f32
    }

    /// Clip gradients for differential privacy
    pub fn clip_gradients(&self, gradients: &[f32], clipping_bound: f64) -> Vec<f32> {
        let norm: f64 = gradients.iter().map(|&x| (x as f64).powi(2)).sum::<f64>().sqrt();

        if norm <= clipping_bound {
            gradients.to_vec()
        } else {
            let scaling_factor = clipping_bound / norm;
            gradients.iter().map(|&x| (x as f64 * scaling_factor) as f32).collect()
        }
    }

    /// Compute gradient sensitivity
    pub fn compute_sensitivity(&self, _gradient_norms: &[f64]) -> f64 {
        // Simplified sensitivity computation
        self.config.noise_config.clipping_norm
    }
}

impl Default for AdvancedPrivacyConfig {
    fn default() -> Self {
        Self {
            epsilon: 1.0,
            delta: 1e-5,
            mechanism: PrivacyMechanism::default(),
            privacy_model: PrivacyModel::default(),
            composition_method: CompositionMethod::default(),
            adaptive_budgeting: true,
            amplification_config: PrivacyAmplificationConfig::default(),
            noise_config: NoiseDistributionConfig::default(),
        }
    }
}

impl Default for PrivacyAmplificationConfig {
    fn default() -> Self {
        Self {
            subsampling_ratio: 0.1,
            shuffling_enabled: true,
            secure_aggregation_amplification: true,
            sampling_method: SamplingMethod::default(),
        }
    }
}

impl Default for NoiseDistributionConfig {
    fn default() -> Self {
        Self {
            noise_multiplier: 1.0,
            clipping_norm: 1.0,
            adaptive_clipping: true,
            per_layer_scaling: false,
            correlated_noise: false,
        }
    }
}

impl Default for PrivacyAccountingConfig {
    fn default() -> Self {
        Self {
            accounting_method: PrivacyAccountingMethod::default(),
            target_epsilon: 10.0,
            target_delta: 1e-5,
            max_rounds: 100,
            privacy_loss_tracking: true,
            composition_analysis: true,
        }
    }
}