Machine Learning Methodologies
Part 5: Machine Learning Methods to Process Datasets With QI Values
Differential Privacy (DP): This mathematical framework gives the ability to control to what extent the model ‘remembers’ and ‘forgets’ potentially sensitive data, which is its big advantage. The most popular concept of ...
Part 4: Standard Ways to Process Datasets with QI Values
K-anonymity: This approach is quite different from the one that I described earlier. With K-anonymity, we’re not aiming to ‘hide’ any data, but rather are softly ‘masking’ the QI values. The most ...
Part 3: Machine Learning Ways to De-Identify Personal Data (Homomorphic Encryption)
-Homomorphic Encryption: The main idea behind homomorphic encryption is that the inferences we make based on computations of encrypted data should be as accurate as if we had used decrypted data. Homomorphic ...
Part 2: Standard Ways to De-Identify Personal Data
Usually, maintainers of the database try to eliminate all channels that could potentially help an attacker leverage queries to gain personal/sensitive information about a specific person. Here are a few examples: Pseudonymization: ...
Part 1: Introduction and Resources of the Data Breach
Terms like ‘sensitive data’ and ‘personal data’ have been floating in the air for a while ever since GDPR, CCPA, and similar privacy acts were introduced to companies across the globe. One ...
