Data Classification Tools

In today’s data-driven world, organizations handle vast amounts of information, including sensitive data. Protecting this sensitive data is crucial to maintain privacy, comply with regulations, and prevent data breaches. Data classification is a fundamental step in safeguarding sensitive information. It involves categorizing data based on its sensitivity level and applying appropriate security measures. In this article, we will explore data classification tools, with a focus on open-source solutions that work with SQL databases.

What is Data Classification?

Data classification is the process of organizing data into categories. In our case there are two categories: sensitive or not. It helps organizations identify which data needs to be secure and to what extent. By classifying data, organizations can apply appropriate security controls, access restrictions, and data handling procedures. Data classification is essential for complying with privacy regulations, such as GDPR and HIPAA, and for preventing unauthorized access to sensitive information.

Open-Source Data Classification Tools

There are several open-source data classification tools available that can help organizations classify data stored in SQL-based databases. Let’s explore some of these tools and see how they can be used to classify sensitive data.

Apache MADlib

Apache MADlib is an open-source library for scalable in-database machine learning. It provides a suite of SQL-based algorithms for data mining and machine learning. This includes data classification algorithms. Here’s an example of how you can use Apache MADlib to classify data as sensitive:

-- Assuming you have a table named "customer_data" with columns "name", "email", "phone", "address", and "is_sensitive"
-- Train the logistic regression model
DROP TABLE IF EXISTS sensitive_data_model;
CREATE TABLE sensitive_data_model AS
SELECT madlib.logregr_train(
'customer_data',
'is_sensitive',
'ARRAY[name, email, phone, address]'
);
-- Predict sensitivity for new data
SELECT madlib.logregr_predict(
'sensitive_data_model',
'ARRAY["John Doe", "[email protected]", "1234567890", "123 Main St"]'
);

In this example, we train a logistic regression model using the madlib.logregr_train function. We train the model on the customer_data table, with the is_sensitive column as the target variable and the name, email, phone, and address columns as features. We use the model to predict the sensitivity of new data using the madlib.logregr_predict function.

Weka

Weka is a popular open-source machine learning workbench written in Java. It offers a wide range of machine learning algorithms, including classification algorithms. Here’s an example of how Weka can be used to classify data as sensitive:

import weka.classifiers.trees.J48;
import weka.core.Instances;

// Assuming you have a database connection named "conn" and a table named "customer_data"
// with columns "name", "email", "phone", "address", and "is_sensitive"

// Load data from the database
String query = "SELECT name, email, phone, address, is_sensitive FROM customer_data";
Instances data = new Instances(conn.createStatement().executeQuery(query));
data.setClassIndex(data.numAttributes() - 1);

// Train the decision tree classifier
J48 classifier = new J48();
classifier.buildClassifier(data);

// Predict sensitivity for new data
String[] newData = {"John Doe", "[email protected]", "1234567890", "123 Main St"};
double predictedSensitivity = classifier.classifyInstance(newData);

In this example, we load data from the customer_data table using a SQL query. Again, we use the data to train a decision tree classifier using the J48 algorithm. The trained classifier predicts the sensitivity of new data.

scikit-learn

scikit-learn is a well-known open-source machine learning library in Python. It provides a comprehensive set of classification algorithms. Here’s an example of how you can use scikit-learn to classify data as sensitive:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import psycopg2

# Assuming you have a database connection named "conn" and a table named "customer_data"
# with columns "name", "email", "phone", "address", and "is_sensitive"

# Load data from the database
query = "SELECT name, email, phone, address, is_sensitive FROM customer_data"
data = pd.read_sql(query, conn)

# Split the data into features and target
X = data[['name', 'email', 'phone', 'address']]
y = data['is_sensitive']

# Train the logistic regression model
model = LogisticRegression()
model.fit(X, y)

# Predict sensitivity for new data
new_data = [["John Doe", "[email protected]", "1234567890", "123 Main St"]]
predicted_sensitivity = model.predict(new_data)

In this example, we load data from the customer_data table using a SQL query and the pd.read_sql function from the pandas library. The data is split into features (X) and the target variable (y). We then train a logistic regression model using the LogisticRegression class from scikit-learn. The trained model can be used to predict the sensitivity of new data.

RapidMiner

This one was acquired by Altair Engineering in September 2022. RapidMiner is a commercial data science platform that offers a graphical user interface for data mining and machine learning tasks. The educational 1 year license is available. Also, they provide this source code download link for AI Studio 2024.0.

It supports various classification algorithms and can connect to SQL databases to access and analyze data. Here’s a high-level overview of how to use RapidMiner to classify data:

Connect to your SQL database using the “Read Database” operator.
Select the table containing the sensitive data and choose the relevant columns.
Use the “Split Data” operator to divide the data into training and testing sets.
Apply a classification algorithm, such as decision trees or logistic regression, to train the model on the training set.
Use the “Apply Model” operator to predict the sensitivity of data in the testing set.
Evaluate the model’s performance using appropriate metrics.

RapidMiner provides a visual workflow designer, making it easier to build and execute classification models without writing code.

KNIME

KNIME (Konstanz Information Miner) is an open-source data analytics platform that allows you to create data flows visually. It offers a wide range of machine learning nodes, including classification algorithms, and can integrate with SQL databases. Here’s a high-level overview of how KNIME can be used to classify data as sensitive:

Use the “Database Reader” node to connect to your SQL database and select the table containing the sensitive data.
Apply the “Column Filter” node to choose the relevant columns for classification.
Use the “Partitioning” node to split the data into training and testing sets.
Apply a classification algorithm, such as decision trees or logistic regression, using the corresponding learner node.
Use the predictor node to predict the sensitivity of data in the testing set.
Evaluate the model’s performance using the “Scorer” node.

KNIME provides a user-friendly interface for building and executing classification workflows, making it accessible to users with limited programming experience.

Conclusion

Data classification is a critical aspect of protecting sensitive information in organizations. Open-source data classification tools, such as Apache MADlib, Weka, scikit-learn, RapidMiner, and KNIME, provide powerful capabilities to classify data stored in SQL-based databases. By leveraging these tools, organizations can identify and categorize sensitive data, apply appropriate security measures, and ensure compliance with data protection regulations.

When implementing data classification, it’s important to consider factors such as the specific requirements of your organization, the nature of your data, and the available resources. Choosing the right tool and approach depends on your organization’s needs and the expertise of your team.

In addition to open-source tools, there are also commercial solutions available for data classification and security. One such solution is DataSunrise, which offers exceptional and flexible tools for data security, audit rules, masking, and compliance. DataSunrise provides a comprehensive suite of features to safeguard sensitive data across various databases and platforms.

If you’re interested in learning more about DataSunrise and how it can help secure your sensitive data, we invite you to contact our team for an online demo. Our experts will be happy to showcase the capabilities of DataSunrise and discuss how we can tailor it to your organization’s specific needs.

Protecting sensitive data is a continuous process that requires ongoing effort and attention. By leveraging data classification tools and implementing robust security measures, organizations can significantly reduce the risk of data breaches and ensure the confidentiality and integrity of their sensitive information.