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What is Data Anonymization?

Data anonymization is the process of modifying personal or sensitive data to remove or mask identifying information, ensuring privacy and compliance with regulations like GDPR and HIPAA. Techniques include data masking, tokenization, and synthetic data generation, making it crucial in AI training, healthcare analytics, and cybersecurity.

Data Integration
Data integration is the process of combining data from multiple sources into a unified view, enabling seamless data analysis and interoperability. It is essential for enterprise data management, business intelligence, and AI applications. Common data integration tools include ETL (Extract, Transform, Load) pipelines, APIs, and middleware solutions.
Emotional Intelligence
Emotional intelligence (EI) refers to the ability of humans or AI systems to recognize, understand, and respond to emotions. AI applications incorporating EI are used in sentiment analysis, customer service automation, and mental health diagnostics.
Fake data
Fake data refers to artificially generated or manipulated information designed to appear real. It is used for testing, training machine learning models, and protecting privacy by replacing sensitive data with synthetic alternatives while maintaining statistical validity for analytics and AI applications.

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