Python案例如何实现数据脱敏？

wen python案例 2026-06-09 257

本文目录导读：

Python案例如何实现数据脱敏？

基础脱敏方法
通用脱敏函数
使用第三方库实现
批量数据处理
完整的脱敏工具类
使用建议

我来介绍几种Python实现数据脱敏的常用方法：

基础脱敏方法

手机号脱敏

import re
def mask_phone(phone):
    """手机号中间四位用****代替"""
    return re.sub(r'(\d{3})\d{4}(\d{4})', r'\1****\2', str(phone))
# 示例
phone = "13812345678"
print(mask_phone(phone))  # 输出: 138****5678

身份证号脱敏

def mask_id_card(id_card):
    """身份证号显示前6位和后4位"""
    if len(id_card) == 18:
        return id_card[:6] + "********" + id_card[-4:]
    return id_card
# 示例
id_card = "110101199001011234"
print(mask_id_card(id_card))  # 输出: 110101********1234

邮箱脱敏

def mask_email(email):
    """邮箱显示第一个字符和域名"""
    parts = email.split('@')
    if len(parts) == 2:
        username, domain = parts
        if len(username) > 1:
            username = username[0] + '*' * (len(username) - 1)
        return f"{username}@{domain}"
    return email
# 示例
email = "zhangsan@example.com"
print(mask_email(email))  # 输出: z*******@example.com

银行卡号脱敏

def mask_bank_card(card_number):
    """银行卡号显示前4位和后4位"""
    card_str = str(card_number).replace(' ', '')
    if len(card_str) >= 8:
        return card_str[:4] + " **** **** " + card_str[-4:]
    return card_str
# 示例
card = "6222021234567890"
print(mask_bank_card(card))  # 输出: 6222 **** **** 7890

通用脱敏函数

def data_masking(data, mask_type='phone'):
    """
    通用数据脱敏函数
    参数:
        data: 需要脱敏的数据
        mask_type: 脱敏类型 (phone, id_card, email, name, bank_card)
    返回:
        脱敏后的数据
    """
    if not data:
        return data
    mask_type = mask_type.lower()
    if mask_type == 'phone':
        return re.sub(r'(\d{3})\d{4}(\d{4})', r'\1****\2', str(data))
    elif mask_type == 'id_card':
        if len(str(data)) == 18:
            return str(data)[:6] + "********" + str(data)[-4:]
        return str(data)
    elif mask_type == 'email':
        parts = str(data).split('@')
        if len(parts) == 2:
            username, domain = parts
            if len(username) > 1:
                username = username[0] + '*' * (len(username) - 1)
            return f"{username}@{domain}"
        return str(data)
    elif mask_type == 'name':
        """姓名脱敏：显示姓氏，名字用*代替"""
        name = str(data)
        if len(name) == 2:
            return name[0] + '*'
        elif len(name) >= 3:
            return name[0] + '*' * (len(name) - 2) + name[-1]
        return name
    elif mask_type == 'bank_card':
        card_str = str(data).replace(' ', '')
        if len(card_str) >= 8:
            return card_str[:4] + "****" + card_str[-4:]
        return card_str
    return data
# 使用示例
print(data_masking("张三", 'name'))  # 输出: 张*
print(data_masking("13812345678", 'phone'))  # 输出: 138****5678

使用第三方库实现

使用 faker 库

# 安装: pip install faker
from faker import Faker
def generate_fake_data(real_data, type='phone'):
    """使用faker生成虚假数据替换真实数据"""
    fake = Faker('zh_CN')
    if type == 'phone':
        return fake.phone_number()
    elif type == 'name':
        return fake.name()
    elif type == 'email':
        return fake.email()
    elif type == 'id_card':
        return fake.ssn()
    return real_data
# 示例
real_phone = "13812345678"
fake_phone = generate_fake_data(real_phone, 'phone')
print(f"真实: {real_phone} -> 虚假: {fake_phone}")

使用 desensitization 库

# 安装: pip install desensitization
from desensitization import Desensitization
des = Desensitization()
# 手机号脱敏
phone = des.phone('13812345678')  # 返回: 138****5678
# 身份证脱敏
id_card = des.id_card('110101199001011234')  # 返回: 110101**********34
# 银行卡脱敏
bank_card = des.bank_card('6222021234567890')  # 返回: 6222 **** **** 7890

批量数据处理

import pandas as pd
def batch_data_masking(df, mask_config):
    """
    批量数据脱敏
    参数:
        df: pandas DataFrame
        mask_config: 字典，格式为{列名: 脱敏类型}
    返回:
        脱敏后的DataFrame
    """
    df_masked = df.copy()
    for column, mask_type in mask_config.items():
        if column in df_masked.columns:
            df_masked[column] = df_masked[column].apply(
                lambda x: data_masking(x, mask_type)
            )
    return df_masked
# 示例使用
data = {
    '姓名': ['张三', '李四', '王五'],
    '手机号': ['13812345678', '13987654321', '13711112222'],
    '身份证': ['110101199001011234', '110101199002022345', '110101199003033456'],
    '邮箱': ['zhangsan@example.com', 'lisi@test.com', 'wangwu@demo.com']
}
df = pd.DataFrame(data)
print("原始数据:")
print(df)
# 配置脱敏规则
mask_config = {
    '姓名': 'name',
    '手机号': 'phone',
    '身份证': 'id_card',
    '邮箱': 'email'
}
df_masked = batch_data_masking(df, mask_config)
print("\n脱敏后数据:")
print(df_masked)

完整的脱敏工具类

class DataMaskingTool:
    """数据脱敏工具类"""
    def __init__(self):
        self.mask_functions = {
            'phone': self._mask_phone,
            'id_card': self._mask_id_card,
            'email': self._mask_email,
            'name': self._mask_name,
            'bank_card': self._mask_bank_card,
            'address': self._mask_address
        }
    def _mask_phone(self, phone):
        return re.sub(r'(\d{3})\d{4}(\d{4})', r'\1****\2', str(phone))
    def _mask_id_card(self, id_card):
        if len(str(id_card)) == 18:
            return str(id_card)[:6] + "********" + str(id_card)[-4:]
        return str(id_card)
    def _mask_email(self, email):
        parts = str(email).split('@')
        if len(parts) == 2:
            username, domain = parts
            if len(username) > 1:
                username = username[0] + '*' * (len(username) - 1)
            return f"{username}@{domain}"
        return str(email)
    def _mask_name(self, name):
        name = str(name)
        if len(name) == 2:
            return name[0] + '*'
        elif len(name) >= 3:
            return name[0] + '*' * (len(name) - 2) + name[-1]
        return name
    def _mask_bank_card(self, card_number):
        card_str = str(card_number).replace(' ', '')
        if len(card_str) >= 8:
            return card_str[:4] + " **** **** " + card_str[-4:]
        return card_str
    def _mask_address(self, address):
        """地址脱敏：只保留省市区"""
        address = str(address)
        if len(address) > 6:
            return address[:6] + '****'
        return address
    def mask(self, data, mask_type='phone'):
        """执行脱敏"""
        if not data:
            return data
        mask_func = self.mask_functions.get(mask_type.lower())
        if mask_func:
            return mask_func(data)
        return data
    def mask_dataframe(self, df, mask_config):
        """批量处理DataFrame"""
        df_masked = df.copy()
        for column, mask_type in mask_config.items():
            if column in df_masked.columns:
                df_masked[column] = df_masked[column].apply(
                    lambda x: self.mask(x, mask_type)
                )
        return df_masked
# 使用示例
tool = DataMaskingTool()
# 单条数据脱敏
print(tool.mask("13812345678", "phone"))
print(tool.mask("张三", "name"))
print(tool.mask("北京市海淀区中关村大街1号", "address"))
# 批量处理DataFrame
# df_masked = tool.mask_dataframe(df, mask_config)