preempt 0.1.23

Sanitize prompts containing PII values, while mainting high utility with cryptographic privacy guarantees

preempt

Prϵϵmpt is a security framework designed to protect personally identifiable information (PII) in text by applying encryption or other privacy-preserving techniques before that data is sent to third-party large language model (LLM) APIs.

Prϵϵmpt achieves high utility for a diverse range of tasks while maintaining cryptographic privacy guarantees. For the experiments and results found in Prϵϵmpt: Sanitizing Sensitive Prompts for LLMs, please refer to this repo.

This is a modular version of Prϵϵmpt, meant to be used as part of other projects.

Setup

1. Install uv following the instructions here.
2. Create a virtual environment with Python 3.10, activate it and add preempt:

uv venv --python 3.10
. ./.venv/bin/activate
uv init
uv add preempt

3. Import Prϵϵmpt methods and classes with from preempt.utils import * to use in your code. See the usage examples below and in demo.ipynb.

If you would like to work with the repo, then clone the repo, navigate to the base folder (preempt) and use the following:

uv venv --python 3.10
. ./.venv/bin/activate
uv sync

If you already have a project in which you would like to use Prϵϵmpt, use either pip install preempt or uv add preempt, depending on the set up.

Usage

Additional usage examples can be found in demo.ipynb.

We will add support for generalized NER and sanitization in the near future.

Complete Usage Example

This is a complete usage example where we sanitize names and currency values. Make sure you either have Universal NER or Llama-3 8B Instruct available.

1. Import NER and Sanitizer objects:

from preempt.ner import *
from preempt.sanitizer import *

2. Initialize a NER and Sanitizer object:

# Load NER object
# ner_model = NER("/path/to/UniNER-7B-all", device="cuda:1")
ner_model = NER("/path/to/Meta-Llama-3-8B-Instruct/", device="cuda:1")

# Load Sanitizer object
sanitizer_name = Sanitizer(ner_model, key = "EF4359D8D580AA4F7F036D6F04FC6A94", tweak = "D8E7920AFA330A73")
sanitizer_money = Sanitizer(ner_model, key = "FF4359D8D580AA4F7F036D6F04FC6A94", tweak = "E8E7920AFA330A73")

# Sentences
sentences = ["Ben Parker and John Doe went to the bank and withdrew $200.", "Adam won $20 in the lottery."]

3. Sanitize names in sentences:

# Sanitizing names
sanitized_sentences, _ = sanitizer_name.encrypt(sentences, entity='Name', epsilon=1)
print("Sanitized sentences:")
print(sanitized_sentences)
"""
Prints:

Sanitized sentences:
['Jay Francois and Lamine Franklin went to the bank and withdrew $200.', 'Elie Vinod won $20 in the lottery.']
"""

4. Sanitize currency values in sanitized_sentences:

# Sanitizing currency values
sanitized_sentences, _ = sanitizer_money.encrypt(sanitized_sentences, entity='Money', epsilon=1)
print("Sanitized sentences:")
print(sanitized_sentences)
"""
Prints:

Sanitized sentences:
['Jay Francois and Lamine Franklin went to the bank and withdrew $769451698.', 'Elie Vinod won $37083668 in the lottery.']
"""

5. Desanitize encrypted names in sanitized_sentences:

# Desanitizing names
desanitized_sentences = sanitizer_name.decrypt(sanitized_sentences, entity='Name', use_cache=True)
print("Desanitized sentences:")
print(desanitized_sentences)
"""
Prints:

Desanitized sentences:
['Ben Parker and John Doe went to the bank and withdrew $769451698.', 'Adam won $37083668 in the lottery.']
"""

6. Desanitize encrypted currency values in desanitized_sentences:

# Desanitizing currency values
desanitized_sentences = sanitizer_money.decrypt(desanitized_sentences, entity='Money', use_cache=True)
print("Desanitized sentences:")
print(desanitized_sentences)
"""
Prints:

Desanitized sentences:
['Ben Parker and John Doe went to the bank and withdrew $200.', 'Adam won $20 in the lottery.']
"""

Extraction

We currently support Universal NER and Llama-3 8B Instruct for NER. We will add support for including your own NER models in the near future.

Initialize a NER class object by passing the path to one of the supported NER models mentioned above:

ner_model = NER("/path/to/Meta-Llama-3-8B-Instruct/", device="cuda:0")

Extract PII values found in a list of target strings using ner_model.extract():

sentences = ["Ben Parker and John Doe went to the bank.", "Who was late today? Adam."]
extracted = ner_model.extract(sentences, entity_type='{Name/Money/Age}')

Sanitization

We currently only support sanitization for names, currency values and age, using either FPE or m-LDP.

Initialize a Sanitizer class object by passing the previously initialized ner_model, a key and tweak parameter (required for the FF3 cipher used for FPE).

sanitizer = Sanitizer(ner_model, key = "EF4359D8D580AA4F7F036D6F04FC6A94", tweak = "D8E7920AFA330A73")

Sanitize a list of target strings using sanitizer.encrypt():

sanitized_sentences, _ = sanitizer.encrypt(sentences, entity='Name', epsilon=1, use_fpe=True, use_mdp=False)

PII values found during NER are stored under sanitizer.new_entities as a nested list.

The mappings between plain text and cipher text PII values are stored under sanitizer.entity_mapping. FPE will typically extract PII values from the sanitized sentences before decryption.

Sanitized sentences can be desanitized using sanitizer.decrypt():

desanitized_sentences = sanitizer.decrypt(sanitized_sentences, entity='Name')

If your NER model can't reliably pick up sanitized attributes, consider setting use_cache=True, to decrypt using stored NER values.

desanitized_sentences = sanitizer.decrypt(sanitized_sentences, entity='Name', use_cache=True)

Sanitizing multiple PII attributes

If you want to sanitize multiple sensitive attributes, create a sanitizer for each category separately.

For more examples, check out demo.ipynb

Usage tips

NER typically works better when the inputs are smaller. Consider breaking a large chunk of text into smaller sentences when using the sanitizer.