Page History

...

• 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, which is just one type of disclosure but the type most applicable to re-id, is when a person's identity is assigned to a record.
• Trying to measure the risk of verification for a dataset
• Quasi-identifiers are those known by an attacker. 
• Delete or encrypt/hash direct identifiers first. What we end up after that is synonymous data.
• For the purposes of re-id risk, we only care about quasi-identifiers.
• A meaningful re-id teaches you something new about the person.
• Attack in two directions - population to sample, sample to population
• Risk is measured by the group size (of 1 = unique)
• Assign a risk value to each record in the dataset.
• To reduce the risk, you can generalize the records and reduce the match rate.
• You can suppress records, remove records, and add noise to reduce the risk of re-id as well.
• generalize - group size gets bigger - risk reduces - maximum (k-anonymity)(public), average (non-public), unicity (proportion of records that are unique in the population)
• You don't want to measure the risk in the data set but measure the risk in the population. The data set is just a sample from the population.
• The group size in the population is the number that's important, but you have to estimate it, since you don't usually have a population registry.
• Once you can estimate the risk properly, you can manage risk in a less conservative way that is still defensive.
• There's no such thing as a probability of zero.
• For releasing public data, a threshold in popular use today is .09. This will give you higher data quality. For particularly sensitive data sets, you would use the more strict threshold of .05.
• risk denominator is not group size in sample but in population
• risk threshold in identifiability spectrum
• privacy-utility tradeofftrade-off
• data transformations - generalization, suppression, addition of noise, microaggregation
• for non-public data, can add controls (privacy, security, contractual)
• motivated intruder attack

...