> For the complete documentation index, see [llms.txt](https://ccc-23.gitbook.io/xin-yong-lian-bai-pi-shu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ccc-23.gitbook.io/xin-yong-lian-bai-pi-shu/4.-xin-yong-zheng-ming-proof-of-credit/4.3-ji-suan-guo-cheng/4.3.2-cha-fen-yin-si-ji-suan.md).

# 4.3.2 差分隐私计算

流程图包含数据处理、噪声注入、以及最终结果的发布等步骤：

<figure><img src="/files/OhkU5iDnLx7WaOcoTQ8k" alt=""><figcaption><p>图5. 差分隐私计算流程图</p></figcaption></figure>

代码示例

<pre><code><strong>import numpy as np
</strong>
def laplace_mechanism(data, sensitivity, epsilon):
    """
    Inject noise using the Laplace mechanism
    :param data: Raw Data
    :param sensitivity: Sensitivity (A constant, representing the sensitivity to changes for each individual)
    :param epsilon: Privacy Parameter
    :return: Result after injecting noise
    """
    noise = np.random.laplace(loc=0, scale=sensitivity/epsilon)
    return data + noise

# Sample Data
original_data = 100
# Sensitivity, e.g., for a count query, is 1.
sensitivity = 1
# Privacy Parameter
epsilon = 0.1

# Inject noise using the Laplace mechanism
noisy_result = laplace_mechanism(original_data, sensitivity, epsilon)

print("Original Data:", original_data)
print("Noisy Result:", noisy_result)
</code></pre>