Privacy Analytics and De-Identification
summary
Describes a commercial system that uses risk-based algorithms to quantify reidentification risk and choose de-identification operations based on context — data fields, recipient, security practices, contracts and intended use — rather than one-size-fits-all Safe Harbor. Claims operationalized statistical de-identification preserves much more data utility, cites a systematic review finding properly deidentified datasets are hard to reidentify, and offers a deidentification maturity model plus tooling to measure and apply thresholds for safe, researchable healthcare data.
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chapters (7)
- 0:00Introduction
- 0:20Competition
- 1:04Riskbased approach
- 2:51Health privacy regimes
- 3:50Other industries
- 5:20Future
- 7:17Myths
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