#!/usr/bin/env python3
"""
Homomorphic Encryption Simulation Engine

Demonstrates HE concepts including BFV-style integer encryption,
CKKS-style approximate encryption, and privacy-preserving computation
patterns. This is an educational simulation — production deployments
should use Microsoft SEAL, OpenFHE, or Concrete.

For production use, install: pip install tenseal
TenSEAL wraps Microsoft SEAL with a Python-friendly API.
"""

import math
import random
from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Optional


@dataclass
class HEParameters:
    """Homomorphic encryption parameter set."""
    scheme: str
    poly_modulus_degree: int
    security_level: int
    max_multiplicative_depth: int
    plain_modulus: Optional[int] = None    # BFV only
    scale: Optional[float] = None          # CKKS only
    coeff_modulus_bits: list[int] = field(default_factory=list)

    @property
    def estimated_ciphertext_size_kb(self) -> float:
        total_bits = sum(self.coeff_modulus_bits)
        # Approximate: 2 * N * total_bits / 8 / 1024
        return 2 * self.poly_modulus_degree * total_bits / 8 / 1024


PARAMETER_PRESETS = {
    "bfv_light": HEParameters(
        scheme="BFV",
        poly_modulus_degree=4096,
        security_level=128,
        max_multiplicative_depth=1,
        plain_modulus=786433,
        coeff_modulus_bits=[40, 40],
    ),
    "bfv_standard": HEParameters(
        scheme="BFV",
        poly_modulus_degree=8192,
        security_level=128,
        max_multiplicative_depth=3,
        plain_modulus=786433,
        coeff_modulus_bits=[60, 40, 40, 60],
    ),
    "ckks_ml": HEParameters(
        scheme="CKKS",
        poly_modulus_degree=16384,
        security_level=128,
        max_multiplicative_depth=5,
        scale=2**40,
        coeff_modulus_bits=[60, 40, 40, 40, 40, 60],
    ),
    "ckks_deep": HEParameters(
        scheme="CKKS",
        poly_modulus_degree=32768,
        security_level=128,
        max_multiplicative_depth=9,
        scale=2**40,
        coeff_modulus_bits=[60, 40, 40, 40, 40, 40, 40, 40, 40, 60],
    ),
}


class SimulatedBFVEncryption:
    """
    Educational simulation of BFV-style integer homomorphic encryption.

    This demonstrates the API pattern and noise behavior of BFV.
    Production code should use Microsoft SEAL or OpenFHE.
    """

    def __init__(self, params: HEParameters):
        self.params = params
        self.noise_budget_bits = sum(params.coeff_modulus_bits) - 10  # Simplified
        self._secret_key = random.getrandbits(256)
        self.operation_log: list[dict] = []

    def encrypt(self, plaintext: int) -> dict:
        """Encrypt an integer value (simulated)."""
        noise = random.gauss(0, 1.0)
        ciphertext = {
            "value": plaintext,  # In real HE, this would be masked
            "noise_level": abs(noise),
            "remaining_budget": self.noise_budget_bits,
            "scheme": "BFV",
            "encrypted": True,
        }
        self._log("encrypt", f"Encrypted value, budget={self.noise_budget_bits} bits")
        return ciphertext

    def decrypt(self, ciphertext: dict) -> int:
        """Decrypt a ciphertext to obtain the integer result."""
        if ciphertext["remaining_budget"] <= 0:
            raise ValueError("Noise budget exhausted — decryption would produce incorrect result")
        self._log("decrypt", f"Decrypted value, remaining budget={ciphertext['remaining_budget']:.1f} bits")
        return ciphertext["value"]

    def add(self, ct1: dict, ct2: dict) -> dict:
        """Add two ciphertexts (noise grows minimally)."""
        result = {
            "value": ct1["value"] + ct2["value"],
            "noise_level": ct1["noise_level"] + ct2["noise_level"],
            "remaining_budget": min(ct1["remaining_budget"], ct2["remaining_budget"]) - 1,
            "scheme": "BFV",
            "encrypted": True,
        }
        self._log("add", f"Ciphertext addition, budget={result['remaining_budget']:.1f} bits")
        return result

    def multiply(self, ct1: dict, ct2: dict) -> dict:
        """Multiply two ciphertexts (noise grows significantly)."""
        noise_growth = 15  # Simplified: multiplication consumes ~15 bits of budget
        new_budget = min(ct1["remaining_budget"], ct2["remaining_budget"]) - noise_growth
        result = {
            "value": ct1["value"] * ct2["value"],
            "noise_level": ct1["noise_level"] * ct2["noise_level"] * 2,
            "remaining_budget": new_budget,
            "scheme": "BFV",
            "encrypted": True,
        }
        self._log("multiply", f"Ciphertext multiplication, budget={result['remaining_budget']:.1f} bits")
        return result

    def add_plain(self, ct: dict, plaintext: int) -> dict:
        """Add a plaintext to a ciphertext."""
        result = {
            "value": ct["value"] + plaintext,
            "noise_level": ct["noise_level"],
            "remaining_budget": ct["remaining_budget"] - 0.5,
            "scheme": "BFV",
            "encrypted": True,
        }
        self._log("add_plain", f"Plaintext addition, budget={result['remaining_budget']:.1f} bits")
        return result

    def multiply_plain(self, ct: dict, plaintext: int) -> dict:
        """Multiply a ciphertext by a plaintext."""
        noise_growth = 5  # Less than ct*ct multiplication
        result = {
            "value": ct["value"] * plaintext,
            "noise_level": ct["noise_level"] * abs(plaintext),
            "remaining_budget": ct["remaining_budget"] - noise_growth,
            "scheme": "BFV",
            "encrypted": True,
        }
        self._log("multiply_plain", f"Plaintext multiplication, budget={result['remaining_budget']:.1f} bits")
        return result

    def _log(self, operation: str, detail: str) -> None:
        self.operation_log.append({
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "operation": operation,
            "detail": detail,
        })


class SimulatedCKKSEncryption:
    """
    Educational simulation of CKKS-style approximate homomorphic encryption.

    CKKS operates on real numbers with controllable approximation error.
    """

    def __init__(self, params: HEParameters):
        self.params = params
        self.noise_budget_bits = sum(params.coeff_modulus_bits) - 10
        self.scale = params.scale or 2**40
        self.operation_log: list[dict] = []

    def encrypt(self, plaintext: float) -> dict:
        noise = random.gauss(0, 1e-7)
        return {
            "value": plaintext + noise,
            "remaining_budget": self.noise_budget_bits,
            "approximation_error": abs(noise),
            "scheme": "CKKS",
            "encrypted": True,
        }

    def decrypt(self, ciphertext: dict) -> float:
        if ciphertext["remaining_budget"] <= 0:
            raise ValueError("Noise budget exhausted")
        return ciphertext["value"]

    def add(self, ct1: dict, ct2: dict) -> dict:
        error = ct1["approximation_error"] + ct2["approximation_error"]
        return {
            "value": ct1["value"] + ct2["value"],
            "remaining_budget": min(ct1["remaining_budget"], ct2["remaining_budget"]) - 1,
            "approximation_error": error,
            "scheme": "CKKS",
            "encrypted": True,
        }

    def multiply(self, ct1: dict, ct2: dict) -> dict:
        noise_growth = 15
        error = (ct1["approximation_error"] * abs(ct2["value"])
                 + ct2["approximation_error"] * abs(ct1["value"])
                 + ct1["approximation_error"] * ct2["approximation_error"])
        return {
            "value": ct1["value"] * ct2["value"],
            "remaining_budget": min(ct1["remaining_budget"], ct2["remaining_budget"]) - noise_growth,
            "approximation_error": error + 1e-6,
            "scheme": "CKKS",
            "encrypted": True,
        }


def encrypted_mean(he: SimulatedBFVEncryption, encrypted_values: list[dict], count: int) -> dict:
    """Compute the mean of encrypted values (using plaintext count for division-free approach)."""
    # Sum all encrypted values
    result = encrypted_values[0]
    for ct in encrypted_values[1:]:
        result = he.add(result, ct)
    # In BFV, we return the sum; division by count happens after decryption
    return result


def encrypted_weighted_sum(he: SimulatedBFVEncryption, encrypted_values: list[dict],
                           weights: list[int]) -> dict:
    """Compute weighted sum: w1*v1 + w2*v2 + ... (weights are plaintext)."""
    terms = [he.multiply_plain(ct, w) for ct, w in zip(encrypted_values, weights)]
    result = terms[0]
    for term in terms[1:]:
        result = he.add(result, term)
    return result


def run_example():
    """Demonstrate homomorphic encryption patterns for Prism Data Systems AG."""

    print("=== Homomorphic Encryption: Parameter Presets ===")
    for name, params in PARAMETER_PRESETS.items():
        print(f"  {name}:")
        print(f"    Scheme: {params.scheme}, N={params.poly_modulus_degree}")
        print(f"    Security: {params.security_level}-bit, Max depth: {params.max_multiplicative_depth}")
        print(f"    Est. ciphertext size: {params.estimated_ciphertext_size_kb:.0f} KB")
        print()

    print("=== BFV Encrypted Integer Arithmetic ===")
    params = PARAMETER_PRESETS["bfv_standard"]
    bfv = SimulatedBFVEncryption(params)

    # Scenario: Compute total revenue from encrypted transaction amounts
    # Prism Data Systems AG encrypts customer transaction values
    # Cloud processor computes the sum without seeing individual amounts
    transaction_amounts = [1500, 2300, 980, 4200, 750]
    print(f"  Plaintext transactions: {transaction_amounts}")
    print(f"  Plaintext sum: {sum(transaction_amounts)}")
    print()

    encrypted_amounts = [bfv.encrypt(amt) for amt in transaction_amounts]
    print(f"  Encrypted {len(encrypted_amounts)} transaction values")
    print(f"  Noise budget after encryption: {encrypted_amounts[0]['remaining_budget']:.0f} bits")

    # Cloud processor computes sum on ciphertexts
    encrypted_sum = encrypted_amounts[0]
    for ct in encrypted_amounts[1:]:
        encrypted_sum = bfv.add(encrypted_sum, ct)

    print(f"  Noise budget after summation: {encrypted_sum['remaining_budget']:.0f} bits")

    # Controller decrypts the result
    decrypted_sum = bfv.decrypt(encrypted_sum)
    print(f"  Decrypted sum: {decrypted_sum}")
    print(f"  Correct: {decrypted_sum == sum(transaction_amounts)}")
    print()

    # Weighted sum example: compute weighted customer satisfaction score
    print("=== BFV Encrypted Weighted Sum ===")
    satisfaction_scores = [4, 5, 3, 4, 5]  # Customer ratings (1-5)
    weights = [10, 20, 15, 25, 30]          # Response weights
    encrypted_scores = [bfv.encrypt(s) for s in satisfaction_scores]
    encrypted_weighted = encrypted_weighted_sum(bfv, encrypted_scores, weights)
    decrypted_weighted = bfv.decrypt(encrypted_weighted)
    expected = sum(s * w for s, w in zip(satisfaction_scores, weights))
    print(f"  Scores: {satisfaction_scores}, Weights: {weights}")
    print(f"  Encrypted weighted sum: {decrypted_weighted}")
    print(f"  Expected: {expected}")
    print(f"  Correct: {decrypted_weighted == expected}")
    print(f"  Noise budget remaining: {encrypted_weighted['remaining_budget']:.0f} bits")
    print()

    print("=== CKKS Encrypted Floating-Point Arithmetic ===")
    ckks_params = PARAMETER_PRESETS["ckks_ml"]
    ckks = SimulatedCKKSEncryption(ckks_params)

    # Scenario: Compute average session duration from encrypted values
    session_durations = [12.5, 45.3, 8.7, 23.1, 67.9, 15.4]
    encrypted_durations = [ckks.encrypt(d) for d in session_durations]

    encrypted_total = encrypted_durations[0]
    for ct in encrypted_durations[1:]:
        encrypted_total = ckks.add(encrypted_total, ct)

    decrypted_total = ckks.decrypt(encrypted_total)
    expected_total = sum(session_durations)
    print(f"  Session durations: {session_durations}")
    print(f"  Encrypted sum: {decrypted_total:.6f}")
    print(f"  Expected sum: {expected_total:.6f}")
    print(f"  Approximation error: {abs(decrypted_total - expected_total):.10f}")
    print(f"  Average (computed after decryption): {decrypted_total / len(session_durations):.2f}")
    print()

    # CKKS multiplication: compute sum of squares
    print("=== CKKS Encrypted Sum of Squares ===")
    values = [3.0, 4.0, 5.0]
    encrypted_vals = [ckks.encrypt(v) for v in values]
    squared = [ckks.multiply(ct, ct) for ct in encrypted_vals]
    sum_sq = squared[0]
    for ct in squared[1:]:
        sum_sq = ckks.add(sum_sq, ct)
    decrypted_sum_sq = ckks.decrypt(sum_sq)
    expected_sum_sq = sum(v * v for v in values)
    print(f"  Values: {values}")
    print(f"  Encrypted sum of squares: {decrypted_sum_sq:.6f}")
    print(f"  Expected: {expected_sum_sq:.6f}")
    print(f"  Noise budget remaining: {sum_sq['remaining_budget']:.0f} bits")
    print()

    print("=== Operation Audit Log (BFV) ===")
    for entry in bfv.operation_log[:8]:
        print(f"  [{entry['operation']}] {entry['detail']}")
    print(f"  ... ({len(bfv.operation_log)} total operations logged)")

    print()
    print("=== Production Deployment Notes ===")
    print("  For production homomorphic encryption, use:")
    print("  - Python: pip install tenseal (wraps Microsoft SEAL)")
    print("  - Python: pip install concrete-python (Zama TFHE)")
    print("  - C++: Microsoft SEAL, OpenFHE, HELib")
    print("  - Rust: tfhe-rs (Zama)")
    print("  This simulation demonstrates API patterns and noise behavior.")
    print("  Real HE libraries provide cryptographic security guarantees.")


if __name__ == "__main__":
    run_example()