1. Attacks against K-anonymity

2. 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

3. Unsorted matching attack against k-anonymity

4. 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

7. 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.