1. Cloud Computing: hadoop Security Design -2009
*All opinions and information are mine and do not represent the view(S) of my employer
Kaveh Noorbakhsh
Kent State: CS
Owen O’Malley | Kan Zhang | Sanjay Radia | Ram Marti | Christopher Harrell | Yahoo!

2. Brief History: Cloud Computing as a Service
1961:
John McCarthy Introduces Concept of Cloud Computing as a business model
1969
ARAPANET
1997
“Cloud Computing” coined by Ramnath Chellappa
1999
Saleforce.com
Enterprise Applications via simple web interface
2002
Amazon Web Services
2004
HDFS & Map/Reduce in Nutch
2006
Google Docs
Amazon EC2
Yahoo hires Doug Cutting
2008
Eucalyptus
1st Open Source AWS API for Private Clouds
OpenNebula
Private and Hybrid clouds
Hadoop hits web scale
2009
MS Azure
Amazon RDS
MySQL supported
2011
Amazon RDS supports Oracle
Office 365

3. Hadoop – Funny Name, Big Impact

4. Map/Reduce An Introduction
Map/Reduce allows computation to scale out over many “cheap” systems rather than one expensive super computer

5. Divide and Conquer Partition Combine “Work” w1 w2 w3 r1 r2 r3 “Result”
“worker”
“worker”
“worker” r1 r2 r3
Combine
“Result”

6. Two Layers
MapReduce:
Code runs here
HDFS:
Data lives here

7. Advantages of the Cloud
Database as a Service = DBaaS
Infrastructure as a Service = Iaas
Software as a Service = SaaS
Platform as a Service = PaaS
Share hardware and energy costs
Share employee costs
Fast spin-up and tear down
Expand quickly to meet demands
Costs ideally proportional to usage
Scalability

8. Cloud Services Spending
Billions of Dollars

9. Cloud vs Total IT Spending
Billions of Dollars

10. Security Challenges of the Cloud
Where is my data living?
You may not know where you data is exactly since the data can be distributed among many physical disks
Where is my data going?
In the cloud, especially in map/reduce, data is constantly in moving from node to node and nodes may be across multiple mini-clouds
Who has access to my data?
There may be other clients using the cloud, as well as, administrators and others who maintained the cloud that could have access to the data if it is not properly protected.

11. Hadoop Security Concerns
Hadoop services do not authenticate users or other services.
(a)  A user can access an HDFS or MapReduce cluster as any other user. This makes it impossible to enforce access control in an uncooperative environment. For example, file permission checking on HDFS can be easily circumvented.
(b)  An attacker can masquerade as Hadoop services. For example, user code running on a MapReduce cluster can register itself as a new TaskTracker.
DataNodes do not enforce any access control on accesses to its data blocks. This makes it possible for an unauthorized client to read a data block as long as she can supply its block ID. It’s also possible for anyone to write arbitrary data blocks to DataNodes.

12. Security Requirements for Hadoop
Users are only allowed to access HDFS files that they have permission to access.
Users are only allowed to access or modify their own MapReduce jobs.
User to service mutual authentication to prevent unauthorized NameN- odes, DataNodes, JobTrackers, or TaskTrackers.
Service to service mutual authentication to prevent unauthorized services from joining a cluster’s HDFS or MapReduce service.
The degradation of performance should be no more than 3%.

13. Proposed Solution – Use Case 1
Accessing Data
1) User/App requests access to a data block.
2) Name Node authenticates and gives the user a block token.
3) User/App uses block token on Data Node to access block for READ, WRITE, COPY or REPLACE.

14. Proposed Solution – Use Case 2
Submitting Jobs
1) A user may obtain a delegation token through Kerberos.
2) Token given to user jobs for subsequent authentication to NameNode as the user.
3) Jobs can use the delegation token to access data that user/app has access to

15. Core Principles Analysis
Confidentiality
Analysis
Users/Apps will only have access to the data blocks they should have via block tokens
Pass

16. Core Principles Analysis
Integrity
Analysis
Data is only available at the block level if the block token matches.
There is an assumption that the data is good because the blocks are not checked
Pass
Fail

17. Core Principles Analysis
Availability
Analysis
Job Tracker and Name Nodes are single points of failure for system.
Tokens persist for a small period of time so the system is resilient to short outages of Name Node and Job Tracker
Fail
Pass

18. Conclusion
The token method for authentication for both data and process access makes sense in a highly distributed system like hadoop. However, the fact that tokens have so much power and are not constantly re-checked leaves this design open to very serious TOCTOU attacks.
As compared to the currently model(aka no security) this represents a major step forward.

19. The End
Questions?