return new EncryptionResult(ciphertext, authTag);
    }

    /**
     * Perform constant-time encryption to prevent timing attacks
     */
    private byte[] performConstantTimeEncryption(Cipher cipher, byte[] message) throws Exception {
        // Pad to fixed block size to prevent timing leaks
        int blockSize = cipher.getBlockSize();
        if (blockSize == 0)
            blockSize = 16; // GCM mode

      slowFactorials[i] = RANDOM_SOURCE.nextInt(factorialBound);
      binomials[i] = RANDOM_SOURCE.nextInt(factorials[i] + 1);
    }
  }

  /** Previous version of BigIntegerMath.factorial, kept for timing purposes. */
  private static BigInteger oldSlowFactorial(int n) {
    if (n <= 20) {
      return BigInteger.valueOf(LongMath.factorial(n));
    } else {
      int k = 20;

* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить
* have the next best performance, after: быть на следующем месте по производительности после
* timing attack: тайминговая атака (clarify `атака по времени` if needed)
* OAuth2 scope: OAuth2 scope (clarify `область` if needed)
* TLS Termination Proxy: прокси-сервер TSL-терминации
* utilize (resources): использовать

 * whereToDiffer} produces no observable change in performance. We want to make sure that the array
 * equals implementation is *not* short-circuiting to prevent timing-based attacks. Being fast is
 * only a secondary goal.
 *
 * @author Kurt Alfred Kluever
 */
@NullUnmarked
public class HashCodeBenchmark {

/**
 * FessIntervalController extends DefaultIntervalController to provide
 * Fess-specific interval control functionality for web crawling operations.
 * This controller manages delays and timing for various crawling states
 * including processing delays, queue waiting times, and new URL discovery.
 */
public class FessIntervalController extends DefaultIntervalController {

	return locked
}

const (
	lockRetryInterval = 50 * time.Millisecond
)

// lockLoop will acquire either a read or a write lock
//
// The call will block until the lock is granted using a built-in
// timing randomized back-off algorithm to try again until successful
func (lm *LRWMutex) lockLoop(ctx context.Context, id, source string, timeout time.Duration, isWriteLock bool) (locked bool) {

  public static final String SERVER = "Server";

  /**
   * The HTTP <a href="https://www.w3.org/TR/server-timing/">{@code Server-Timing}</a> header field
   * name.
   *
   * @since 23.6
   */
  public static final String SERVER_TIMING = "Server-Timing";

  /**
   * The HTTP <a href="https://www.w3.org/TR/service-workers/#update-algorithm">{@code

这类似于：

```Python
if not (credentials.username == "stanleyjobson") or not (credentials.password == "swordfish"):
    # Return some error
    ...
```

但使用 `secrets.compare_digest()`，可以防御**时差攻击**，更加安全。

### 时差攻击 { #timing-attacks }

什么是**时差攻击**？

假设攻击者试图猜出用户名与密码。

他们发送用户名为 `johndoe`，密码为 `love123` 的请求。

然后，Python 代码执行如下操作：

```Python
if "johndoe" == "stanleyjobson" and "love123" == "swordfish":
    ...
```

這大致等同於：

```Python
if not (credentials.username == "stanleyjobson") or not (credentials.password == "swordfish"):
    # 回傳錯誤
    ...
```

但藉由使用 `secrets.compare_digest()`，可以防禦一種稱為「計時攻擊」的攻擊。

### 計時攻擊 { #timing-attacks }

什麼是「計時攻擊」呢？

想像有攻擊者在嘗試猜測使用者名稱與密碼。

他們送出一個帶有使用者名稱 `johndoe` 與密碼 `love123` 的請求。

接著，你的應用程式中的 Python 程式碼等同於：

```Python
if "johndoe" == "stanleyjobson" and "love123" == "swordfish":
    ...

        // Check for leaks
        resourceManager.checkForLeaks();

        Map<String, Object> stats = resourceManager.getStatistics();
        // The leak counter may or may not increment depending on GC timing
        assertNotNull(stats.get("totalLeaks"));
    }

    private void createLeakyResource() {
        TestResource resource = new TestResource("leaky");
        resourceManager.registerResource(resource);