bc-en-de-coder 0.0.18

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

Encode and decode values in string using the encode_str() and decode_str() methods.

from bc_endecoder.replacing import BaseCoder

bc = BaseCoder()

text = '''
        This is a dummy text with value 200,100,150,250.
        We need to protect these values.
        '''

encoded_text,encodings = bc.encode_str(text)  #encode_str takes 1 paramter which is the text and returns the encoded text and encoding
print("Encoded Text : \n",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 = bc.decode_str(encoded_text,encodings)  #decode_str takes 2 parameters which are the encoded_text and encoding and returns the original text
print("\nOriginal Text : \n",original_text)

Output

Encoded Text : 
 
        This is a dummy text with value 4858416350,7636580946,0858875814,8301435677.
        We need to protect these values.
        

Original Text : 
 
        This is a dummy text with value 200,100,150,250.
        We need to protect these values.

Encoding and Decoding values in Dataframe

Encode and decode values in Dataframe using the encode_df() and decode_df() methods.

from bc_endecoder.replacing import BaseCoder
import pandas as pd
import numpy as np

bc = BaseCoder()

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 = bc.encode_df(df)  #encode_df takes 1 paramter which is Dataframe and returns the encoded dataframe and encodings
print("Encoded_df : \n",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 = bc.decode_df(encoded_df,encoding)  #decode_str takes 2 parameters which are the encoded_df and encoding and returns the original df
print("\nOriginal_df : \n",original_df)

Output

Encoded_df :    
              Name  Age                  City  Salary
        0    Alice  8623770624       New York  0197705789
        1      Bob  5223314994  San Francisco  9743912420
        2  Charlie  1795473060    Los Angeles  8982145407
        3    David  6439787181        Chicago  6618233087
        4      Eva  4699492207          Miami  6680877680

Original_df : 
             Name   Age          City  Salary
        0    Alice  25       New York  60000
        1      Bob  30  San Francisco  80000
        2  Charlie  22    Los Angeles  55000
        3    David  35        Chicago  90000
        4      Eva  28          Miami  70000

Encoding and Decoding values with a ratio in Dataframe, Json or String

Encode and decode values with a ratio in Dataframe, Json or String using the encode_df_ratio() and decode_df_ratio() methods.

from bc_endecoder.replacing import BaseCoder
import pandas as pd
import numpy as np

bc = BaseCoder()

json_data = {
  "key1": 10,
  "key2": 20,
  "key3": "Hello",
  "key4": 3.14,
  "key5": [1, 2, 3],
  "key6": {"nested_key": "nested_value"},
  "key8": "2022-01-01",
  "key9": None,
  "key10": {"sub_key1": 5, "sub_key2": "world"},
  "key11": [4.5, 6.7, 8.9],
  "key12": False,
  "key13": "42",
  "key14": ["apple", "banana", "cherry"],
  "key15": {"nested_key2": [1, 2, 3]},
  "key16": 7.77,
  "key17": "test",
  "key18": {"sub_key3": "value3", "sub_key4": 10},
  "key19": [True, False],
  "key20": 12345
}

ratio = 56 #this is the ratio for which we want to encode the data, it can be any number except 0 and 1

encoded_data = bc.encode_in_ratio(json_data,ratio)  #encode_in_ratio takes 2 paramter which is Data and the ratio number, and returns the encoded data
print("Encoded data : \n", 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 = bc.decode_in_ratio(encoded_data,ratio)  #decode_str takes 2 parameters which are the encoded_data and encoding and returns the original json
print("Original data : \n",original_data)

Output

Encoded data : 
 {'key1': 560, 'key2': 1120, 'key3': 'Hello', 'key4': 175.84, 'key5': [56, 112, 168], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 280, 'sub_key2': 'world'}, 'key11': [252.0, 375.2, 498.40000000000003], 'key12': 0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [56, 112, 168]}, 'key16': 435.12, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 560}, 'key19': [56, 0], 'key20': 691320}

Original data : 
 {'key1': 10.0, 'key2': 20.0, 'key3': 'Hello', 'key4': 3.14, 'key5': [1.0, 2.0, 3.0], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 5.0, 'sub_key2': 'world'}, 'key11': [4.5, 6.7, 8.9], 'key12': 0.0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [1.0, 2.0, 3.0]}, 'key16': 7.7700000000000005, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 10.0}, 'key19': [1.0, 0.0], 'key20': 12345.0}