Implementing a user privacy risk assessment system based on text content involves multiple steps and technologies. The following is a complete Python code example covering text representation based on BERT, cluster-based text privacy system construction, privacy information extraction based on named entity recognition, and text privacy quantification based on information entropy.
1. Install the required libraries
First, make sure you have installed the following Python libraries:
pip install transformers scikit-learn numpy pandas spacy python -m spacy download en_core_web_sm
2. Import the required libraries
import numpy as np import pandas as pd from transformers import BertTokenizer, BertModel from import KMeans import spacy from collections import Counter import math
3. BERT-based text representation
def get_bert_embeddings(texts, model_name='bert-base-uncased'):
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)
inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy()
return embeddings
4. Construction of text privacy system based on clustering
def cluster_texts(embeddings, n_clusters=5):
kmeans = KMeans(n_clusters=n_clusters)
(embeddings)
return kmeans.labels_
5. Privacy information extraction based on naming entity recognition
def extract_private_info(texts):
nlp = ("en_core_web_sm")
private_info = []
for text in texts:
doc = nlp(text)
entities = [ for ent in if ent.label_ in ['PERSON', 'GPE', 'ORG', 'DATE']]
private_info.append(entities)
return private_info
6. Quantification of text privacy based on information entropy
def calculate_entropy(private_info):
all_entities = [entity for sublist in private_info for entity in sublist]
entity_counts = Counter(all_entities)
total_entities = len(all_entities)
entropy = 0.0
for count in entity_counts.values():
probability = count / total_entities
entropy -= probability * (probability, 2)
return entropy
7. User privacy leakage risk assessment
def assess_privacy_risk(texts):
# Step 1: Get BERT embeddings
embeddings = get_bert_embeddings(texts)
# Step 2: Cluster texts
labels = cluster_texts(embeddings)
# Step 3: Extract private information
private_info = extract_private_info(texts)
# Step 4: Calculate information entropy
entropy = calculate_entropy(private_info)
# Step 5: Assess privacy risk based on entropy
if entropy > 2.0:
return "High Privacy Risk"
elif entropy > 1.0:
return "Medium Privacy Risk"
else:
return "Low Privacy Risk"8. Test code
if __name__ == "__main__":
# Example texts
texts = [
"My name is John Doe and I live in New York.",
"I work at Google and my birthday is on 1990-01-01.",
"The meeting is scheduled for next Monday at 10 AM.",
"Alice and Bob are working on the project together."
]
# Assess privacy risk
risk_level = assess_privacy_risk(texts)
print(f"Privacy Risk Level: {risk_level}")
9. Operation results
After running the above code, you will get an output similar to the following:
Privacy Risk Level: High Privacy Risk
10. Code explanation
BERT text representation: Use the BERT model to convert text into vector representation.
Text clustering: Use the KMeans clustering algorithm to cluster text and build a text privacy system.
Named entity recognition: Use the SpaCy library to extract privacy information in text (such as person names, place names, organization names, dates, etc.).
Information entropy calculation: calculates the information entropy of extracted privacy information, used to quantify privacy risks.
Privacy risk assessment: Assess the privacy risk level based on the value of information entropy.
11. Further optimization
Model selection: You can try to use other pre-trained models (such as RoBERTa, DistilBERT, etc.) to improve the accuracy of text representation.
Clustering algorithm: You can try other clustering algorithms (such as DBSCAN, hierarchical clustering, etc.) to build a more refined text privacy system.
Privacy information extraction: You can extend SpaCy's entity identification rules, or use other NLP tools (such as NLTK, Stanford NLP, etc.) to extract more types of privacy information.
This is the article about Python's privacy information extraction and evaluation based on text content. For more related Python text privacy information extraction content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!