Shuffle rules are designed to randomly re-distribute the data in a table column. This effect is similar to the shuffling of a deck of cards (hence the name Shuffle Rule). Each Shuffle Rule affects only a specified column in a specified table. All other columns in the table remain unaffected.
For example if your EMP table originally contained the data...
EMP_ID EMP_FIRSTNAME EMP_LASTNAME 1 Bill Smith 2 Sally Jones 3 William Green 4 Ben White 5 Jane Doe 6 Amanda Smith 7 Fred Black 8 Jim Archer
and a Shuffle Rule was applied to the EMP_LASTNAME column then after executing the Masking Set the content of the EMP table might look like...
EMP_ID EMP_FIRSTNAME EMP_LASTNAME 1 Bill Black 2 Sally White 3 William Smith 4 Ben Jones 5 Jane Archer 6 Amanda Green 7 Fred Smith 8 Jim Doe
One of the interesting benefits of shuffle rules is that if applied against a column of number data they can scramble the contents of a table column while still preserving the total sum of the column. Since no new data has been added, (the numbers have just been moved around), the total of all of the numbers will remain the same.
There can be any number of Shuffle rules on any columns in any table in a schema. If you apply a Shuffle Rule to a column that is used in a primary key or unique index then the index will have to be dropped while the shuffle rule is executing because the shuffle process will not guarantee uniqueness until the shuffling has been complete.
Shuffle rules do not use any options. They affect all columns in every row in the specified table. Shuffle rules cannot be used against tables containing a single row (the shuffle will be meaningless) and really should not be used in tables with a small number of rows. If there are too few rows the shuffle process will still work but the small number of rows makes it easier to manually "guess" which column data was originally associated with each row.