Privacy and Compression: Secret Savings

In highly regulated industries (Finance, Health, Government), you cannot send raw user data to a cloud LLM. You must redact or mask Personally Identifiable Information (PII).

Most developers treat PII Redaction as a "Security burden." But if done correctly, it is a Token Efficiency Opportunity.

By replacing long, sensitive strings (e.g., "The patient Johnathan Montgomery Smith born on Jan 14, 1978...") with short placeholders (e.g., [P_01]), you achieve two goals:

1. Security: No sensitive data leaves your infrastructure.
2. Efficiency: You have compressed 15 tokens into 1.

In this final lesson of Module 18, we learn the Place-Holder Mapping pattern and the Minified Redaction strategy.

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1. The PII Mapping Pattern

1. Step 1 (Local): Scan text for PII (Names, Emails, SSNs) using a library like Presidio or Regex.
2. Step 2 (Local): Store the mapping in a local memory-map.
   • Johnathan Smith -> [P1]
   • jsmith@gmail.com -> [E1]
3. Step 3 (Remote): Send the Minified Block to the LLM.
4. Step 4 (Local): When the LLM responds, "De-minify" by subbing the names back in.

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2. Token Diversity ROI

PII is often repetitive. A name might appear 20 times in a document.

• Verbatim: 3 tokens/name × 20 = 60 tokens.
• Mapped: 1 token/placeholder × 20 = 20 tokens.

Savings: 66%. In a 50-page legal document with hundreds of references to "Party A" and "Party B," this mapping can save thousands of tokens while ensuring 100% compliance.

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3. Implementation: The Privacy Compressor (Python)

Python Code: Mapping and Re-Injective

import re

def compress_privacy(text):
    # Mapping table
    mapping = {}
    
    # 1. Identity Email
    emails = re.findall(r"[\w\.-]+@[\w\.-]+", text)
    for i, email in enumerate(emails):
        placeholder = f"[E{i}]"
        mapping[placeholder] = email
        text = text.replace(email, placeholder)
        
    return text, mapping

def decompress_privacy(llm_output, mapping):
    for placeholder, original in mapping.items():
        llm_output = llm_output.replace(placeholder, original)
    return llm_output

# Cycle: 
# "Contact jdoe@me.com" -> "Contact [E0]" -> "Reply to [E0]" -> "Reply to jdoe@me.com"

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4. The 'Token Hashing' Pattern (Advanced)

For extremely sensitive data that needs to be "Consistent" across requests but never revealed (e.g., a Database ID), use a Short Hash.

• db_id_9876543210 -> h_123

This keeps the model's focus on the Logic of the ID without move the actual ID through the cloud context.

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5. Summary and Key Takeaways

1. Redaction is Compression: Short placeholders use fewer tokens than long names.
2. Deterministic Mapping: Keep the "Secret Sauce" on your server; send only the "Recipe" (Placeholders) to the AI.
3. Consistency: Use the same placeholder for the same entity throughout the conversation to maintain the model's reasoning.
4. Compliance ROI: You save money on tokens while also satisfying your legal department.

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Exercise: The Privacy Switch

1. Take a business email with multiple project names, employee names, and dates.
2. Manually replace every Name with [N] and every Project with [P].
3. Count the tokens of the new text.
4. Ask a model: "Based on this text, what is the status of project [P1]?"
5. Verify: Does the model still understand the relationships?

• (Result: Yes, LLMs are excellent at reasoning about variables/placeholders).

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Congratulations on completing Module 18! Your systems are now both private and efficient.