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Python utils.AddGaussianNoise方法代码示例

本文整理汇总了Python中differential_privacy.dp_sgd.dp_optimizer.utils.AddGaussianNoise方法的典型用法代码示例。如果您正苦于以下问题:Python utils.AddGaussianNoise方法的具体用法?Python utils.AddGaussianNoise怎么用?Python utils.AddGaussianNoise使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在differential_privacy.dp_sgd.dp_optimizer.utils的用法示例。


在下文中一共展示了utils.AddGaussianNoise方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: sanitize

# 需要导入模块: from differential_privacy.dp_sgd.dp_optimizer import utils [as 别名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.utils import AddGaussianNoise [as 别名]
def sanitize(self, x, eps_delta, sigma=None,
               option=ClipOption(None, None), tensor_name=None,
               num_examples=None, add_noise=True):
    """Sanitize the given tensor.

    This santize a given tensor by first applying l2 norm clipping and then
    adding Gaussian noise. It calls the privacy accountant for updating the
    privacy spending.

    Args:
      x: the tensor to sanitize.
      eps_delta: a pair of eps, delta for (eps,delta)-DP. Use it to
        compute sigma if sigma is None.
      sigma: if sigma is not None, use sigma.
      option: a ClipOption which, if supplied, used for
        clipping and adding noise.
      tensor_name: the name of the tensor.
      num_examples: if None, use the number of "rows" of x.
      add_noise: if True, then add noise, else just clip.
    Returns:
      a pair of sanitized tensor and the operation to accumulate privacy
      spending.
    """

    if sigma is None:
      # pylint: disable=unpacking-non-sequence
      eps, delta = eps_delta
      with tf.control_dependencies(
          [tf.Assert(tf.greater(eps, 0),
                     ["eps needs to be greater than 0"]),
           tf.Assert(tf.greater(delta, 0),
                     ["delta needs to be greater than 0"])]):
        # The following formula is taken from
        #   Dwork and Roth, The Algorithmic Foundations of Differential
        #   Privacy, Appendix A.
        #   http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
        sigma = tf.sqrt(2.0 * tf.log(1.25 / delta)) / eps

    l2norm_bound, clip = option
    if l2norm_bound is None:
      l2norm_bound, clip = self._default_option
      if ((tensor_name is not None) and
          (tensor_name in self._options)):
        l2norm_bound, clip = self._options[tensor_name]
    if clip:
      x = utils.BatchClipByL2norm(x, l2norm_bound)

    if add_noise:
      if num_examples is None:
        num_examples = tf.slice(tf.shape(x), [0], [1])
      privacy_accum_op = self._accountant.accumulate_privacy_spending(
          eps_delta, sigma, num_examples)
      with tf.control_dependencies([privacy_accum_op]):
        saned_x = utils.AddGaussianNoise(tf.reduce_sum(x, 0),
                                         sigma * l2norm_bound)
    else:
      saned_x = tf.reduce_sum(x, 0)
    return saned_x 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:60,代码来源:sanitizer.py

示例2: sanitize

# 需要导入模块: from differential_privacy.dp_sgd.dp_optimizer import utils [as 别名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.utils import AddGaussianNoise [as 别名]
def sanitize(self, x, eps_delta, sigma=None,
               option=ClipOption(None, None), tensor_name=None,
               num_examples=None, add_noise=True):
    """Sanitize the given tensor.

    This santize a given tensor by first applying l2 norm clipping and then
    adding Gaussian noise. It calls the privacy accountant for updating the
    privacy spending.

    Args:
      x: the tensor to sanitize.
      eps_delta: a pair of eps, delta for (eps,delta)-DP. Use it to
        compute sigma if sigma is None.
      sigma: if sigma is not None, use sigma.
      option: a ClipOption which, if supplied, used for
        clipping and adding noise.
      tensor_name: the name of the tensor.
      num_examples: if None, use the number of "rows" of x.
      add_noise: if True, then add noise, else just clip.
    Returns:
      a pair of sanitized tensor and the operation to accumulate privacy
      spending.
    """
    if sigma is None:
      # pylint: disable=unpacking-non-sequence
      eps, delta = eps_delta
      with tf.control_dependencies(
          [tf.Assert(tf.greater(eps, 0),
                     ["eps needs to be greater than 0"]),
           tf.Assert(tf.greater(delta, 0),
                     ["delta needs to be greater than 0"])]):
        # The following formula is taken from
        #   Dwork and Roth, The Algorithmic Foundations of Differential
        #   Privacy, Appendix A.
        #   http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
        sigma = tf.sqrt(2.0 * tf.log(1.25 / delta)) / eps

    l2norm_bound, clip = option
    if l2norm_bound is None:
      l2norm_bound, clip = self._default_option
      if ((tensor_name is not None) and
          (tensor_name in self._options)):
        l2norm_bound, clip = self._options[tensor_name]
    if clip:
      x = utils.BatchClipByL2norm(x, l2norm_bound)

    if add_noise:
      if num_examples is None:
        num_examples = tf.slice(tf.shape(x), [0], [1])
      privacy_accum_op = self._accountant.accumulate_privacy_spending(
          eps_delta, sigma, num_examples)
      with tf.control_dependencies([privacy_accum_op]):
        saned_x = utils.AddGaussianNoise(tf.reduce_sum(x, 0),
                                         sigma * l2norm_bound)
    else:
      saned_x = tf.reduce_sum(x, 0)
    return saned_x 
开发者ID:ratschlab,项目名称:RGAN,代码行数:59,代码来源:sanitizer.py


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