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HBase >> mail # dev >> Efficiently wiping out random data?

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Re: Efficiently wiping out random data?
One more followup on this, after talking to some security-types:

 - The issue isn't wiping out all data for a customer; it's wiping out *specific* data. Using the "forget an encryption key" method would then mean separate encryption keys per row, which isn't feasible most of the time. (Consider information that becomes classified but didn't used to be, for example.)
 - In some cases, decryption can still happen without keys, by brute force or from finding weaknesses in the algorithms down the road. Yes, I know that the brute force CPU time is measured in eons, but never say never; we can easily decrypt things now that were encrypted with the best available algorithms and keys 40 years ago. :)

So for cases where it counts, a "secure delete" means no less than writing over the data with random strings. It would be interesting to add features to HBase / HDFS that passed muster for stuff like this; for example, an HDFS secure-delete<http://www.ghacks.net/2010/08/26/securely-delete-files-with-secure-delete/> command, and an HBase secure-delete that does all of: add delete marker, force major compaction, and run HDFS secure-delete.


On Jun 20, 2013, at 7:39 AM, Jean-Marc Spaggiari wrote:

Correct, that's another way. Just need to have one encryption key per
customer. And all what is written into HBase, over all the tables, is
encrypted with that key.

If the customer want to have all its data erased, just erased the key,
and you have no way to retrieve anything from HBase even if it's still
into all the tables. So now you can emit all the deletes required, and
that will be totally deleted on the next regular major compaction...

There will be a small impact on regular reads/write since you will
need to read the key first, but them a user delete will be way more
2013/6/20 lars hofhansl <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>:
IMHO the "proper" of doing such things is encryption.

0-ing the values or even overwriting with a pattern typically leaves traces of the old data on a magnetic platter that can be retrieved with proper forensics. (Secure erase of SSD is typically pretty secure, though).
For such use cases, files (HFiles) should be encrypted and the decryption keys should just be forgotten at the appropriate times.
I realize that for J-D's specific use case doing this at the HFile level would be very difficult.

Maybe the KVs' values could be stored encrypted with a user specific key. Deleting the user's data then means to forget that users key.
-- Lars

From: Matt Corgan <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>>
Sent: Wednesday, June 19, 2013 2:15 PM
Subject: Re: Efficiently wiping out random data?
Would it be possible to zero-out all the value bytes for cells in existing
HFiles?  They keys would remain, but if you knew that ahead of time you
could design your keys so they don't contain important info.
On Wed, Jun 19, 2013 at 11:28 AM, Ian Varley <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>> wrote:

At least in some cases, the answer to that question ("do you even have to
destroy your tapes?") is a resounding "yes". For some extreme cases (think
health care, privacy, etc), companies do all RDBMS backups to disk instead
of tape for that reason. (Transaction logs are considered different, I
guess because they're inherently transient? Who knows.)

The "no time travel" fix doesn't work, because you could still change that
code or ACL in the future and get back to the data. In these cases, one
must provably destroy the data.

That said, forcing full compactions (especially if they can be targeted
via stripes or levels or something) is an OK way to handle it, maybe
eventually with more ways to nice it down so it doesn't hose your cluster.


On Jun 19, 2013, at 11:27 AM, Todd Lipcon wrote:

I'd also question what exactly the regulatory requirements for deletion
are. For example, if you had tape backups of your Oracle DB, would you have
to drive to your off-site storage facility, grab every tape you ever made,
and zero out the user's data as well? I doubt it, considering tapes have
basically the same storage characteristics as HDFS in terms of inability to
random write.

Another example: deletes work the same way in most databases -- eg in
postgres, deletion of a record just consists of setting a record's "xmax"
column to the current transaction ID. This is equivalent to a tombstone,
and you have to wait for a VACUUM process to come along and actually delete
the record entry. In Oracle, the record will persist in a rollback segment
for a configurable amount of time, and you can use a Flashback query to
time travel and see it again. In Vertica, you also set an "xmax" entry and
wait until the next merge-out (like a major compaction).

Even in a filesystem, deletion doesn't typically remove data, unless you
use a tool like srm. It just unlinks the inode from the directory tree.

So, if any of the above systems satisfy their use case, then HBase ought to
as well. Perhaps there's an ACL we could add which would allow/disallow
users from doing time travel more than N seconds in the past..  maybe that
would help allay fears?


On Wed, Jun 19, 2013 at 8:12 AM, Jesse Yates <[EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>
<mailto:[EMAIL PROTECTED]>>wrote:

Chances are that date isn't completely "random". For instance, with a user
they are likely to have an id in their row key, so doing a filtering (with
a custom scanner) major compaction would clean that up. With Sergey's
compaction stuff coming in you could break that out even further and only
have to compact a small set of files to get that removal.

So it's hard, but as its not our direct use case, it's gonna be a few extra

On Wednesday, June 19, 2013, Kevin O'dell wrote:

Yeah, the immutable nature of HDFS is biting us here.
On Wed, Jun 19, 2013 at 8:46 AM, Jean-Daniel Cryans <j
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB