PII detection and redaction in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - PII detection and redaction

This pipeline detects personal information in text and replaces it with safe placeholders. It helps protect privacy by automatically finding and hiding sensitive data like names, emails, and phone numbers.

Data Flow - 5 Stages

1Input Text

1000 sentences x variable length→Raw text containing personal information→1000 sentences x variable length

"Hello, my name is Alice and my email is [e-mail]."

↓

2Text Preprocessing

1000 sentences x variable length→Lowercase, remove extra spaces, tokenize sentences→1000 sentences x tokens per sentence

["hello", ",", "my", "name", "is", "alice", "and", "my", "email", "is", "[e-mail]", "."]

↓

3Feature Extraction

1000 sentences x tokens→Convert tokens to word embeddings (numeric vectors)→1000 sentences x tokens x 300 features

[[0.12, -0.05, ..., 0.33], ..., [0.45, 0.01, ..., -0.22]]

↓

4Model Prediction

1000 sentences x tokens x 300 features→Named Entity Recognition model tags tokens as PII or not→1000 sentences x tokens with PII tags

[('hello', 'O'), ('my', 'O'), ('name', 'O'), ('is', 'O'), ('alice', 'B-PER'), ('and', 'O'), ('my', 'O'), ('email', 'O'), ('is', 'O'), ('[e-mail]', 'B-EMAIL'), ('.', 'O')]

↓

5Redaction

1000 sentences x tokens with PII tags→Replace PII tokens with placeholders→1000 sentences x tokens with redacted text

"Hello, my name is [PERSON] and my email is [EMAIL]."

Training Trace - Epoch by Epoch

Loss
0.9 |***************
0.7 |************
0.5 |********
0.3 |*****
0.1 |**
    +----------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.65	Model starts learning to identify PII with moderate accuracy.
2	0.60	0.78	Loss decreases and accuracy improves as model learns patterns.
3	0.45	0.85	Model shows good ability to detect PII entities.
4	0.35	0.90	Further improvement with more precise tagging.
5	0.30	0.92	Model converges with high accuracy and low loss.

Prediction Trace - 5 Layers

Layer 1: Input Text

Layer 2: Text Preprocessing

Layer 3: Feature Extraction

Layer 4: Model Prediction

Layer 5: Redaction

Model Quiz - 3 Questions

What happens to the text after the redaction stage?

A Text is translated to another language

B PII is replaced with placeholders

C Text is summarized

D Text is converted to audio

Key Insight

This visualization shows how a model learns to find and hide personal information in text. The training improves the model's ability to tag sensitive data, enabling automatic redaction to protect privacy.

Practice

1. What is the main purpose of PII detection in text data?

easy

A. To increase the size of the dataset

B. To improve the speed of text processing

C. To find personal information to protect privacy

D. To translate text into different languages

Solution

1. Step 1: Understand PII detection

   PII detection is about finding personal information like names, emails, or phone numbers in text.

2. Step 2: Identify the purpose

   The goal is to protect privacy by recognizing sensitive data that should not be shared openly.

3. Final Answer:

   To find personal information to protect privacy -> Option C

4. Quick Check:

   PII detection = find personal info [OK]

Hint: PII detection means finding personal info to keep it safe [OK]

Common Mistakes:

• Confusing PII detection with data translation
• Thinking it speeds up processing
• Believing it increases dataset size

2. Which of the following is the correct way to redact an email address in text?

easy

A. Replace the email with <EMAIL_REDACTED>

B. Delete the entire sentence containing the email

C. Change the email to a random number

D. Highlight the email in bold

Solution

1. Step 1: Understand redaction

   Redaction means hiding sensitive info by replacing it with a placeholder, not deleting or changing it randomly.

2. Step 2: Choose the correct method

   Replacing the email with a clear placeholder like <EMAIL_REDACTED> keeps the text readable and safe.

3. Final Answer:

   Replace the email with <EMAIL_REDACTED> -> Option A

4. Quick Check:

   Redaction = replace sensitive info with placeholder [OK]

Hint: Redact by replacing sensitive info with clear placeholders [OK]

Common Mistakes:

• Deleting whole sentences instead of redacting
• Replacing emails with unrelated data
• Highlighting instead of hiding

3. Given this Python code snippet for PII redaction:

import re
text = 'Contact me at [e-mail] or 123-456-7890.'
redacted = re.sub(r'\S+@\S+\.\S+', '<EMAIL_REDACTED>', text)
print(redacted)

What will be the output?

medium

A. Contact me at [e-mail] or 123-456-7890.

B. Contact me at [e-mail] or <EMAIL_REDACTED>.

C. Contact me at <EMAIL_REDACTED> or <EMAIL_REDACTED>.

D. Contact me at <EMAIL_REDACTED> or 123-456-7890.

Solution

1. Step 1: Understand the regex pattern

   The pattern '\S+@\S+\.\S+' matches email addresses (non-space chars @ non-space chars . non-space chars).

2. Step 2: Apply substitution

   The code replaces the email with '<EMAIL_REDACTED>' but leaves the phone number unchanged.

3. Final Answer:

   Contact me at <EMAIL_REDACTED> or 123-456-7890. -> Option D

4. Quick Check:

   Email replaced, phone unchanged = Contact me at <EMAIL_REDACTED> or 123-456-7890. [OK]

Hint: Regex replaces emails only, phone stays same [OK]

Common Mistakes:

• Thinking phone number is replaced
• Misreading regex pattern
• Assuming no replacement happens

4. You wrote this code to redact phone numbers:

import re
text = 'Call 555-1234 or 555-5678.'
redacted = re.sub(r'\d{3}-\d{4}', '<PHONE_REDACTED>', text)
print(redacted)

But the output is:
'Call 555-1234 or 555-5678.'
What is the likely error?

medium

A. The regex pattern is incorrect and does not match the phone numbers

B. The re.sub function is missing the text argument

C. The print statement is missing parentheses

D. The text variable is empty

Solution

1. Step 1: Check regex pattern against phone format

   The pattern '\d{3}-\d{4}' matches numbers like '555-1234', but the phone numbers might have different formats or extra spaces.

2. Step 2: Confirm if pattern matches text

   If the phone numbers have area codes or spaces, the pattern won't match, so no replacement occurs.

3. Final Answer:

   The regex pattern is incorrect and does not match the phone numbers -> Option A

4. Quick Check:

   Regex mismatch causes no replacement [OK]

Hint: Check regex matches exact phone format in text [OK]

Common Mistakes:

• Assuming re.sub syntax error
• Forgetting parentheses in print (Python 3+)
• Thinking text is empty without checking

5. You want to redact both emails and phone numbers in a text using Python. Which combined regex pattern correctly matches emails and US phone numbers like '123-456-7890'?

hard

A. r'\d{3}-\d{4}|\S+@\S+\.\S+'

B. r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}'

C. r'\S+@\S+\.\S+\d{3}-\d{3}-\d{4}'

D. r'\S+@\S+\.\S+&\d{3}-\d{3}-\d{4}'

Solution

1. Step 1: Understand regex for emails and phones

   The email pattern '\S+@\S+\.\S+' matches emails; '\d{3}-\d{3}-\d{4}' matches US phone numbers like '123-456-7890'.

2. Step 2: Combine patterns with OR operator

   Using '|' between patterns matches either emails or phone numbers separately.

3. Step 3: Evaluate options

   r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}' correctly uses '|' to combine patterns; r'\d{3}-\d{4}|\S+@\S+\.\S+' reverses order but still works; r'\S+@\S+\.\S+\d{3}-\d{3}-\d{4}' concatenates patterns (wrong); r'\S+@\S+\.\S+&\d{3}-\d{3}-\d{4}' uses '&' which is invalid in regex.

4. Final Answer:

   r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}' -> Option B

5. Quick Check:

   Use '|' to combine regex patterns [OK]

Hint: Use '|' to combine email and phone regex patterns [OK]

Common Mistakes:

• Concatenating patterns without '|'
• Using invalid regex operators like '&'
• Mixing order but forgetting OR operator