/**
 * 混币（CoinJoin）交易模拟
 * 模拟多输入多输出的匿名集混淆效果
 */
interface UTXO {
  txid: string;
  vout: number;
  value: bigint;     // 以 satoshi 为单位
  owner: string;
  scriptPubKey: string;
}

interface CoinJoinParticipant {
  inputs: UTXO[];
  changeOutput: { value: bigint; scriptPubKey: string } | null;
  min anonymitySet: number;  // 最小匿名集要求
}

function createCoinJoin(
  participants: CoinJoinParticipant[],
  denomination: bigint,    // 混币面额（所有人必须相等）
): {
  inputs: UTXO[];
  outputs: { value: bigint; scriptPubKey: string }[];
  anonymitySet: number;
  entropy: number;         // 香农熵： $\sum p_i \log_2 p_i$
} {
  const allInputs: UTXO[] = [];
  const allOutputs: { value: bigint; scriptPubKey: string }[] = [];
  
  for (const p of participants) {
    // 所有参与者必须提供至少等于 denomination 的输入
    const totalInput = p.inputs.reduce((s, u) => s + u.value, 0n);
    if (totalInput < denomination) {
      throw new Error(`Participant lacks sufficient funds for denomination ${denomination}`);
    }
    allInputs.push(...p.inputs);
    
    // 等值混币输出
    allOutputs.push({ value: denomination, scriptPubKey: p.changeOutput!.scriptPubKey });
    
    // 找零输出（如果有剩余）
    const change = totalInput - denomination;
    if (change > 0n && p.changeOutput) {
      allOutputs.push({ value: change, scriptPubKey: p.changeOutput.scriptPubKey });
    }
  }
  
  // 匿名集 = 等值输出的数量
  const anonymitySet = participants.length;
  
  // 香农熵 = $\sum_{i=1}^k \frac{1}{k} \log_2(k) = \log_2(k)$
  const p = 1 / anonymitySet;
  const entropy = -anonymitySet * (p * Math.log2(p));
  
  return { inputs: allInputs, outputs: allOutputs, anonymitySet, entropy };
}

// 示例：4人各混1 BTC
const participants: CoinJoinParticipant[] = [
  { inputs: [{txid:'a1', vout:0, value:1200000000n, owner:'Alice', scriptPubKey:'pk_A'}], changeOutput: {value:0n, scriptPubKey:'pk_A_new'}, minAnonymitySet: 3 },
  { inputs: [{txid:'b1', vout:0, value:1500000000n, owner:'Bob', scriptPubKey:'pk_B'}], changeOutput: {value:0n, scriptPubKey:'pk_B_new'}, minAnonymitySet: 3 },
  { inputs: [{txid:'c1', vout:0, value:1000000000n, owner:'Carol', scriptPubKey:'pk_C'}], changeOutput: {value:0n, scriptPubKey:'pk_C_new'}, minAnonymitySet: 3 },
  { inputs: [{txid:'d1', vout:0, value:1100000000n, owner:'Diana', scriptPubKey:'pk_D'}], changeOutput: {value:0n, scriptPubKey:'pk_D_new'}, minAnonymitySet: 3 },
];

const result = createCoinJoin(participants, 1000000000n);
console.log(`匿名集: ${result.anonymitySet}, 熵:${result.entropy.toFixed(2)} bits`);
// 输出：匿名集: 4, 熵: 2.00 bits
// 攻击者猜对的概率 $P = 1/4 = 25\%$

/**
 * 隐匿地址（Stealth Address）核心机制模拟
 * 基于椭圆曲线 Diffie-Hellman 的一次性地址
 */

// 简化中：我们用大整数模拟椭圆曲线点乘的"密钥派生"效果
// 实际中需要 secp256k1 点加/点乘；这里用模运算的代数性质来保持逻辑

interface ECPoint {
  x: bigint;
  y: bigint;
}

const P = 2n**256n - 2n**32n - 977n; // secp256k1 p
const G = 79n; // 简化的"生成元"——实际为曲线基点，这里用常数代表代换

function scalarMult(scalar: bigint, point: bigint): bigint {
  // 简化：实际应为椭圆曲线标量乘法
  // 这里用模运算模拟 "scalar * G mod P"
  return (scalar * point) % P;
}

function hashToScalar(data: string): bigint {
  // 简化：使用字符串哈希转整数
  let h = 0n;
  for (let i = 0; i < data.length; i++) {
    h = (h * 31n + BigInt(data.charCodeAt(i))) % P;
  }
  return h;
}

interface StealthKeyPair {
  privateScanKey: bigint;  // 扫描私钥
  privateSpendKey: bigint; // 花费私钥
  publicViewKey: bigint;   // 对应的公钥（用于派生地址）
}

function generateStealthAddress(
  recipientViewKey: bigint,  // Bob 的公钥/视图密钥
  recipientSpendKey: bigint,  // Bob 的公钥/花费密钥（对于简化版，假设相同）
  nonce: bigint,              // 交易索引/随机数，确保可链接性
): {
  oneTimeAddress: bigint;
  ephemeralPubKey: bigint;    // R，包含在交易中让 Bob 检出
  viewTag: string;            // 快速扫描用的短标签
} {
  // Alice 生成临时私钥 r
  const r = (nonce * 7919n + 1234567n) % P; // 简化随机数生成
  const R = scalarMult(r, G);
  
  // 共享秘密 S = r * P_recipient
  const S = scalarMult(r, recipientViewKey);
  
  // 一次性地址公钥: P_one_time = H(S || n) + P_spend
  const h = hashToScalar(`${S}:${nonce}`);
  const oneTimeAddress = (scalarMult(h, G) + recipientSpendKey) % P;
  
  // viewTag 用于 O(1) 快速匹配扫描（选择共享秘密的最高8位）
  const viewTag = `0x${(S >> 200n).toString(16).padStart(16, '0')}`;
  
  return { oneTimeAddress, ephemeralPubKey: R, viewTag };
}

// Bob 扫描算法
// Bob 持有 x_view（视图的密钥）
function checkIfMine(
  viewKeyPrivate: bigint,
  ephemeralPubKey: bigint,
  nonce: bigint,
  oneTimeAddress: bigint,
  spendKeyPublic: bigint,
): { isMine: boolean; possibleAddress: bigint } {
  // Bob 恢复 S' = x * R
  const S_prime = scalarMult(viewKeyPrivate, ephemeralPubKey);
  
  // 计算他期望的该交易的一次性地址
  const h = hashToScalar(`${S_prime}:${nonce}`);
  const expectedAddress = (scalarMult(h, G) + spendKeyPublic) % P;
  
  return { isMine: expectedAddress === oneTimeAddress, possibleAddress: expectedAddress };
}

// --- 演示 ---
const BobViewKey = 123456789n;
const BobSpendKey = 987654321n; // 简化：实际中由独立推导

// Alice 要发送给 Bob
const tx = generateStealthAddress(BobViewKey, BobSpendKey, 42n);
console.log(`生成的隐匿地址: ${tx.oneTimeAddress.toString(16).slice(0, 16)}...`);

// Bob 扫描这三笔交易
const txs = [
  generateStealthAddress(BobViewKey, BobSpendKey, 42n),
  generateStealthAddress(99999999n, 111111111n, 43n), // 不是给 Bob 的
  generateStealthAddress(BobViewKey, BobSpendKey, 44n),
];

let myTxCount = 0;
for (let i = 0; i < txs.length; i++) {
  const check = checkIfMine(BobViewKey, txs[i].ephemeralPubKey, [42n, 43n, 44n][i], txs[i].oneTimeAddress, BobSpendKey);
  if (check.isMine) myTxCount++;
}
console.log(`Bob 检出 ${myTxCount} 笔属于自己的交易`);
// 输出：Bob 检出 2 笔属于自己的交易（第0笔和第2笔）