使用层级进行数据泛化

Python 中的数据隐私与匿名化

Rebeca Gonzalez

Data engineer

数据泛化

可将数据值替换为更不精确的值。

  • "Dancer" -> "Artist"
  • "Archaeologist" -> "Scientist"
  • "Allergist" -> "Doctor"
Python 中的数据隐私与匿名化

用层级进行数据泛化

图片显示左侧为职业列表，右侧为更高层级的类别。

Python 中的数据隐私与匿名化

难民身份数据集

# Explore dataset
refugee_df.head()

     Nationality    Gender    RefugeeStatus    Year
0    russian        F         0                2010
1    colombian      M         0                2008
2    vietnamese     F         1                2008
3    korean         M         0                2010
4    ethiopian      F         1                2012
Python 中的数据隐私与匿名化

探索数据集

# Set k value to 4
k = 4


# Calculate how many unique combinations are for nationality and gender
df_count = df.groupby(['Nationality', 'Gender']).size().reset_index(name='Count')


# Filter the rows that have a count value less than k
df_count[df_count['Count'] < k]

      Nationality   Gender    Count
4     colombian     F         2
7     cuban         M         2
12    liberian      F         2
14    mexican       F         3
15    mexican       M         3
17    russian       M         1
20    ukranian      F         3
21    ukranian      M         1
Python 中的数据隐私与匿名化

用层级进行数据泛化

# See the combination counts
df['Nationality'].value_counts()

belarusian    14
korean        14
ethiopian     10
chinese       10
vietnamese     9
taiwanese      9
colombian      6
cuban          6
russian        6
mexican        6
liberian       6
ukranian       4
Name: Nationality, dtype: int64
Python 中的数据隐私与匿名化

用层级进行数据泛化

# Create hierarchies for origin countries
hierarchies = {'Europe': ['ukranian','russian','belarusian'], 
               'America':['mexican','colombian','cuban'],
               'Asia': ['taiwanese','korean','chinese'],
               'Africa': ['ethiopian','liberian']}
Python 中的数据隐私与匿名化

用层级进行数据泛化

# Creating hierachy father for each contry
origin_hierarchy = {}
for (key, countries) in hierarchies.items():
    for country in countries:
        origin_hierarchy[country] = key

origin_hierarchy

{'belarusian': 'Europe',
 'chinese': 'Asia',
 'colombian': 'America',
 'cuban': 'America',
 'ethiopian': 'Africa',
 'korean': 'Asia',
 'liberian': 'Africa',
 'mexican': 'America',
 'russian': 'Europe',
 'taiwanese': 'Asia',
 'ukranian': 'Europe'}
Python 中的数据隐私与匿名化

用层级进行数据泛化

# Apply origin_hierarchy hierarchy generalization to Nationality
df['Nationality_generalized'] = df['Nationality'].map(origin_hierarchy)


# Explore resulting dataset
df.head()

     Nationality   Gender    RefugeeStatus    Year    Nationality_generalized
0    korean        M         1                2019    Asia
1    ethiopian     M         1                2003    Africa
2    cuban         M         0                2015    America
3    cuban         F         0                2001    America
4    korean        M         0                2013    Asia
Python 中的数据隐私与匿名化

用层级进行数据泛化

# Calculate how many unique combinations are for Nationality_generalized and Gender
df_count = df.groupby(['Nationality_generalized', 'Gender']).size()
                                                            .reset_index(name='Count')

# Filter the combinations that appear less than k times
df_count[df_count['Count'] < k] 

    Nationality_generalized    Gender    Count
Python 中的数据隐私与匿名化

更多关于 K-匿名

类型：

  • 约束法
  • Mondrian 多维方法
Python 中的数据隐私与匿名化

K-匿名有多安全？

表格展示当敏感属性缺乏多样性时可能发生的隐私泄露。示例：Bob 在表中，满足所有特征，并属于一个所有人患同一种病的 k 人组，因此可确定他患有该病。

Python 中的数据隐私与匿名化

让我们来练习！

Python 中的数据隐私与匿名化

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