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Exploring US Census microdata, tackling privacy issues, and anonymization. Exercise A delves into quasi-identifiers, anonymization methods, identification risks, and differential privacy. Exercise B involves data loading, k-anonymity, histograms, adding noise for privacy, computing private averages, and analyzing privacy parameter impacts.

License: MIT License

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data-anonymization differential-privacy encryption gdpr pseudonymization quasi-identifiers jupyter-notebook k-anonymity python3 gaussian-mechanism

census-privacy-analysis's Introduction

Welcome to Lefteris' GitHub Profile! ๐Ÿ‘‹

Hey there! I'm Leftรฉris Soรบflas, known as @Lefteris-Souflas in the GitHub community. I'm passionate about Business Analytics, Data Science, Data Engineering, and Database Administration. Here's a bit more about me:

  • ๐Ÿ‘€ I thrive on exploring data, uncovering insights, and crafting innovative solutions that drive business success.

  • ๐ŸŽ“ I hold a Master of Science degree in Business Analytics, empowering me with a robust skill set in statistical analysis, machine learning, and data visualization.

  • ๐Ÿ’ก With hands-on experience in data engineering and database administration, I excel at designing efficient data pipelines and optimizing database performance.

  • ๐Ÿ’ž๏ธ I'm eager to collaborate with fellow enthusiasts in these domains. Whether it's co-creating impactful projects, exchanging insights, or staying updated on industry trends, I'm all in for meaningful collaboration.

  • ๐Ÿ“ซ You can easily connect with me via my GitHub account or reach out to me on LinkedIn. I'm always keen to connect with like-minded individuals and explore new opportunities for growth and innovation.

Feel free to delve into my repositories and don't hesitate to reach out if you have any questions or innovative ideas for collaboration. Let's synergize our expertise and propel the boundaries of innovation together! ๐Ÿš€

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