Data Anonymization

Overview

A Data Anonymization workshop is designed to provide participants with a comprehensive understanding of data anonymization techniques, best practices, and regulatory considerations. The workshop aims to equip attendees with the knowledge and skills necessary to effectively anonymize sensitive data while preserving its utility for analysis and research purposes.

Throughout the workshop, there will be interactive sessions, hands-on exercises, and discussions to reinforce the concepts learned. Participants will have the opportunity to apply anonymization techniques to sample datasets and discuss practical implementation challenges. Participants gain a deeper understanding of privacy-preserving data practices, learn practical techniques for anonymization, and become equipped to navigate the legal and ethical challenges associated with handling sensitive data.

By the end of the workshop, participants will have a solid understanding of data anonymization concepts and different anonymization techniques.

Target audience

• Data Privacy and Security Professionals
• Data Engineers and Data Governance Professionals
• Data Scientists and Analysts
• Legal and Compliance Professionals

Prerequisites

• Basic knowledge of data management and regulations related to data (e.g. GDPR)

Content

• Introduction to Data Anonymization:
  • Overview of data anonymization and its importance
  • Legal and ethical considerations related to data privacy and protection
• Anonymization Techniques:
  • Different methods and algorithms for data anonymization
  • Approaches for masking personally identifiable information (PII)
• Evaluating Anonymization Effectiveness
  • Metrics for assessing the privacy risk and utility of anonymized data
  • Methods for measuring re-identification risk
  • Balancing privacy and data utility trade-offs
• Practical Anonymization Strategies:
  • Anonymization techniques for structured and unstructured data
  • De-identification methods for various data types (e.g., numerical, categorical, text)
  • Data generalization, suppression, perturbation, and other anonymization approaches
• Regulatory Compliance and Privacy Laws:
  • Understanding relevant data protection regulations (e.g., GDPR, CCPA)
  • Compliance requirements for anonymized data
  • Legal considerations for sharing and using anonymized data

• Challenges and Limitations:
  • Limitations and vulnerabilities of anonymization techniques
  • Risks associated with re-identification attacks
  • Mitigation strategies and ongoing research in data anonymization
• Anonymization Tools and Technologies:
  • Overview of software tools and libraries for data anonymization
  • Hands-on demonstrations of popular anonymization platforms
  • Integration of anonymization techniques into data processing workflows
• Case Studies and Practical Applications
  • Real-world case studies highlighting anonymization challenges and solutions
  • Group discussions on anonymization in specific industries and use cases
  • Best practices for implementing anonymization in different contexts

For all inquiries regarding education, please contact us at learn@croz.net.

Our experience:

Cloud-based machine learning powered anonymization solution for DATEV.