Attacks against K-anonymity

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Presentation transcript:

Attacks against K-anonymity

Unsorted matching attack against k-anonymity Problem This attack is based on the order in which tuples appear in the released table. Solution By randomly sorting the tuples of the solution table

Unsorted matching attack against k-anonymity

Complementary release attack against k-anonymity Suppose Table GT1 is released. If subsequently GT3 is also released, then the k-anonymity protection will no longer hold, even though the tuple positions are randomly determined in both tables

Temporal attack against k-anonymity Problem Data collections are dynamic. Tuples are added, changed, and removed constantly. As a result, releases of generalized data over time can be subject to a temporal inference attack. Solution Either all of the attributes of first table would be considered a quasiidentifier for subsequent releases Subsequent releases themselves would be based on first table.