bc-en-de-coder 0.0.14

Data Protecting Package

BC-EnDeCoder

BC-EnDeCoder is a Python library that provides a secure way to encode and decode data for use with Large Language Models (LLM). The library allows you to protect sensitive information by passing a fake dummy value, which is then encoded and decoded to and from its original form after receiving a response from the LLM.

Features

• Secure Encoding and Decoding: Protect your sensitive data by encoding it with a fake dummy value and decoding it back to the original form after interacting with an LLM.

• Easy Integration: Simple and easy-to-use functions for encoding and decoding data, making it convenient to integrate into your projects.

• Customizable Encoding Parameters: Fine-tune the encoding process with customizable parameters to suit your specific use case.

Installation

To install BC-EnDeCoder, you can use the following pip command:

pip install bc-en-de-coder 

How it Works

BC-EnDeCoder facilitates a secure interaction with LLMs through a three-step process:

• Encoding with a Dummy Value: Sensitive data is encoded using a fake value, providing an added layer of security during transmission to an LLM.

• Interaction with LLM: The encoded data is then passed to the LLM for analysis or processing.

• Decoding the Response: Upon receiving the LLM's response, BC-EnDeCoder decodes it, revealing the original information without compromising its security.

Encoding and Decoding values in string

from bc_endecoder.replacing import Decoder, Encoder

text = '''
        This is a dummy text with value 200,100,150,250.
        You want to protect these values. We can do so using encode_str() and decode_str() methods.
        '''

encoded_text,encoding = Encoder().encode_str(text)  #encode_str takes 1 paramter which is the text and returns the encoded text and encoding
print(encoded_text)

## encoded_text can be passed to GPT and after getting back the response, it will be decoded using decode_str() method

original_text = Decoder().decode_str(encoded_text,encoding)  #decode_str takes 2 parameters which are the encoded_text and encoding and returns the original text
print(original_text)

Encoding and Decoding values in Dataframe

from bc_endecoder.replacing import Decoder, Encoder
import pandas as pd
import numpy as np

#protect all the values in the dataframe
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 22, 35, 28],
    'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago', 'Miami'],
    'Salary': [60000, 80000, 55000, 90000, 70000]
        }
df = pd.DataFrame(data)

encoded_df,encoding = Encoder().encode_df(df)  #encode_df takes 1 paramter which is Dataframe and returns the encoded dataframe and encodings
print(encoded_df)

## encoded_df can be passed to GPT and after getting back the response, it will be decoded using decode_df() method

original_df = Decoder().decode_df(encoded_df,encoding)  #decode_str takes 2 parameters which are the encoded_df and encoding and returns the original df
print(original_df)

Encoding and Decoding values with a ratio in Dataframe or String

from bc_endecoder.replacing import Decoder, Encoder
import pandas as pd
import numpy as np

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 22, 35, 28],
    'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago', 'Miami'],
    'Salary': [60000, 80000, 55000, 90000, 70000]
}
df = pd.DataFrame(data)

ratio = 25

encoded_data = Encoder().encode_in_ratio(df,ratio)  #encode_in_ratio takes 2 paramter which is Data and the ratio number, and returns the encoded data
print(encoded_data)

## encoded_data can be passed to GPT and after getting back the response it will be decoded using decode_df() method

original_data = Decoder().decode_in_ratio(encoded_data,ratio)  #decode_str takes 2 parameters which are the encoded_data and encoding and returns the original df
print(original_data)