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Presentation by Khaled El Emam on Re-identification Risk Measurement - slides, slides with annotations.

• Questions for group were how to pick a threat model, which identifiers to be concerned about, and how to establish a risk threshold for public data release.
• Apply stratification principles to structured data. If you have unstructured data, structure it first.
• Identity disclosure is when a person's identity is assigned to a record.
• Trying to measure the risk of verification for a dataset
• Quasi-identifiers are known by an attacker
• Delete or encrypt/hash direct identifiers first. What we end up after that is synonymous data.
• definition of identity disclosure
• quasi-identifiers
• attack in two directions - population to sample, sample to population
• risk measure by group size (of 1 = unique)
• generalize - group size gets bigger - risk reduces - maximum (k-anonymity)(public), average (non-public), unicity
• risk denominator is not group size in sample but in population
• risk threshold in identifiability spectrum
• privacy-utility tradeoff
• data transformations - generalization, suppression, addition of noise, microaggregation
• for non-public data, can add controls (privacy, security, contractual)
• motivated intruder attack

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