Privacy-Preserving ML
Differential privacy accounting, federated learning, secure aggregation, and the utility cost of each guarantee.
12concepts
110flashcards
89minutes of reading
- 01 PII Detection and Redaction Pipelines Why regular expressions catch the easy half, how the recall-utility tradeoff differs between training corpora and live traffic, and the re-identification risk that survives field-level redaction.
- 02 k-Anonymity and Re-identification Why removing names does not anonymise a table, what k-anonymity guarantees and the attacks it does not stop, how the Netflix Prize data was de-anonymised from a handful of ratings, and why differential privacy replaced syntactic anonymity as the formal standard.