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__all__ = [
"mask_email_udf", "mask_name_udf", "mask_date_udf",
"mask_ssn_udf", "mask_itin_udf", "mask_phone_udf"
]
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
import re
from datetime import datetime
# Masking functions
def mask_email(value):
if value and "@" in value:
user, domain = value.split("@")
return "***@" + domain
return None
def mask_name(value):
if value:
return value[0] + "***"
return None
def mask_date(value):
try:
dt = datetime.strptime(value, "%Y-%m-%d")
return dt.strftime("***-**-%d")
except:
return None
def mask_ssn(value):
if value and re.match(r"\d{3}-\d{2}-\d{4}", value):
return "***-**-" + value[-4:]
return None
def mask_itin(value):
if value and re.match(r"9\d{2}-7\d-\d{4}", value):
return "***-**-" + value[-4:]
return None
def mask_phone(value):
if value and re.match(r"\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}", value):
return "***-***-" + value[-4:]
return None
# UDFs for Spark
mask_email_udf = udf(mask_email, StringType())
mask_name_udf = udf(mask_name, StringType())
mask_date_udf = udf(mask_date, StringType())
mask_ssn_udf = udf(mask_ssn, StringType())
mask_itin_udf = udf(mask_itin, StringType())
mask_phone_udf = udf(mask_phone, StringType())
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