Checking for data changes across a very large table

I'd like some general guidance on a security issue please. This may belong in the another list so please push me there if appropriate.

We have an application design which includes a potential 2 billion row table (A). When the application user kicks off an analysis process, there is a requirement to perform a check on that table to verify that the data within hasn't changed.

The initial intent was to hold a single "checksum" or equivalent in another small table (B). This checksum value would have been calculated over a subset of row values held within the 2bn row table. When the user chooses to kick off their processing, the application would re-calculate this checksum for table A in real time and compare the new result with the original value stored in table B. We might obtain the subset from table A like this:

SELECT * FROM (
SELECT *,
(row_number() OVER( ORDER BY "ID"))%10000000 AS rn
FROM "TableA") aa
WHERE aa.rn=1 LIMIT 20000

... and then perform an operation on column data from that subset (in this example, a 100,000th of the original data) to calculate a checksum value. Obviously the data within the subset would have to have been affected in order for a difference to be detected.

It is a requirement that users have to have direct access to the DB (I hate this, but am overruled). I envisage that non-admin users would have read-only access, and the above validation would be an extra check on the data.

But my main concern is performance - I fear that this won't be able to perform (in a few seconds rather than minutes). Does the general approach make any sense? Can anyone suggest a better starting point?

This is Postgres 9.0. The minimum hardware spec is small and Windows-based (64-bit, Core i7 processor, 8GB RAM, single 1TB hard disk). My evaluation DB has ended up with the following config tweaks:

shared_buffers 2048 MB
work_mem 2047 MB
checkpoint_segments 128

Thanks in advance.

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