# Privacy-Preserving Technologies in Data

{
  "id": "T10764",
  "title": "Privacy-Preserving Technologies in Data",
  "url": "/topics/T10764",
  "kind": "topic-directory",
  "aeo_cohort": "topic-directory",
  "provider": "openalex",
  "revision": "e7a7f1abe05f1c65",
  "catalogue_revision": "f050b5d4780b1289",
  "numeric_address": "36174758265654355",
  "address_version": "morton4x16-v1",
  "coordinates": [
    3,
    17,
    1702,
    10764
  ],
  "answer_status": "Directory metadata; local answer requires separate source review",
  "truth_status": "Subject descriptor, not a factual answer or a demonstrated knowledge gap"
}

This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis.

Directory metadata is not a factual answer or evidence of a global knowledge gap.

Source: https://openalex.org/T10764

No owner-reviewed local answer yet.