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600.412.Lecture06

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ragibhasan

CS 600.412 Security and Privacy in Cloud Computing

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Security and Privacy in Cloud Computing Ragib HasanJohns Hopkins Universityen.600.412 Spring 2010 Lecture 6 03/22/2010
Verifying Computations in a Cloud 3/22/2010 Scenario User sends her data processing job to the cloud. Clouds provide dataflow operation as a service (e.g., MapReduce, Hadoop etc.) Problem: Users have no way of evaluating the correctness of results en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 2
DataFlow Operations 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 3 Properties High performance, in-memory data processing Each node performs a particular function Nodes are mostly independent of each other Examples MapReduce, Hadoop, System S, Dryad
How do we ensure DataFlow operation results are correct? 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 4 Goals ,[object Object]
To determine the nature of their malicious action
To evaluate the quality of output dataDu et al., RunTest: Assuring Integrity of Dataflow Processing in Cloud Computing Infrastructures, AsiaCCS 2010
Possible Approaches Re-do the computation Check memory footprint of code execution Majority voting Hardware-based attestation Run-time attestation 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 5
RunTest: Randomized Data Attestation Idea For some data inputs, send it along multiple dataflow paths Record and match all intermediate results from the matching nodes in the paths Build an attestation graph using node agreement Over time, the graph shows which node misbehave (always or time-to-time) 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 6
Attack Model Data model: Input deterministic DataFlow (i.e., same input to a function will always produce the same output) Data processing is stateless (e.g., selection, filtering) Attacker: Malicious or compromised cloud nodes Can produce bad results always or some time Can collude with other malicious nodes to provide same bad result 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 7
Attack Model (scenarios) Parameters b_i = probability of providing bad result c_i = probability of providing the same bad result as another malicious node Attack scenarios NCAM: b_i = 1, c_i = 0 NCPM: 0 < b_i <1, c_i = 0 FTFC: b_i = 1, c_i = 1 PTFC: 0< b_i < 1, c_i = 0 PTPC: 0< b_i < 1, 0 < c_i < 1 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 8
Integrity Attestation Graph Definition: Vertices: Nodes in the DataFlow paths Edges: Consistency relationships. Edge weight: fraction of consistent output of all outputs generated from same data items 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 9
Consistency Clique Complete subgraph of an attestation graph which has 2 or more nodes All nodes always agree with each other (i.e., all edge weights are 1) 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 10 2 1 3 5 4
How to find malicious nodes Intuitions Honest nodes will always agree with each other to produce the same outputs, given the same data Number of malicious nodes is less than half of all nodes 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 11
Finding Consistency Cliques: BK Algorithm Goal: find the maximal clique in the attestation graph Technique: Apply Bron-Kerbosch algorithm to find the maximal clique(s) (see better example at Wikipedia) Any node not in a maximal clique of size k/2 is a malicious node 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 12 Note: BK algorithm is NP-Hard Authors proposed 2 optimizations to make it run quicker
Identifying attack patterns 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 13 NCAM PTFC/NCPM PTFC FTFC
Inferring data quality Quality = 1 – (c/n) where n = total number of unique data items c = total number of duplicated data with inconsistent results 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 14
Evaluation Extended IBM System S Experiments: Detection rate Sensitivity to parameters Comparison with majority voting 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 15
Evaluation 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 16 NCPM (b=0.2, c=0) Different misbehavior probabilities

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600.412.Lecture06

  • 1. Security and Privacy in Cloud Computing Ragib HasanJohns Hopkins Universityen.600.412 Spring 2010 Lecture 6 03/22/2010
  • 2. Verifying Computations in a Cloud 3/22/2010 Scenario User sends her data processing job to the cloud. Clouds provide dataflow operation as a service (e.g., MapReduce, Hadoop etc.) Problem: Users have no way of evaluating the correctness of results en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 2
  • 3. DataFlow Operations 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 3 Properties High performance, in-memory data processing Each node performs a particular function Nodes are mostly independent of each other Examples MapReduce, Hadoop, System S, Dryad
  • 4.
  • 5. To determine the nature of their malicious action
  • 6. To evaluate the quality of output dataDu et al., RunTest: Assuring Integrity of Dataflow Processing in Cloud Computing Infrastructures, AsiaCCS 2010
  • 7. Possible Approaches Re-do the computation Check memory footprint of code execution Majority voting Hardware-based attestation Run-time attestation 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 5
  • 8. RunTest: Randomized Data Attestation Idea For some data inputs, send it along multiple dataflow paths Record and match all intermediate results from the matching nodes in the paths Build an attestation graph using node agreement Over time, the graph shows which node misbehave (always or time-to-time) 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 6
  • 9. Attack Model Data model: Input deterministic DataFlow (i.e., same input to a function will always produce the same output) Data processing is stateless (e.g., selection, filtering) Attacker: Malicious or compromised cloud nodes Can produce bad results always or some time Can collude with other malicious nodes to provide same bad result 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 7
  • 10. Attack Model (scenarios) Parameters b_i = probability of providing bad result c_i = probability of providing the same bad result as another malicious node Attack scenarios NCAM: b_i = 1, c_i = 0 NCPM: 0 < b_i <1, c_i = 0 FTFC: b_i = 1, c_i = 1 PTFC: 0< b_i < 1, c_i = 0 PTPC: 0< b_i < 1, 0 < c_i < 1 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 8
  • 11. Integrity Attestation Graph Definition: Vertices: Nodes in the DataFlow paths Edges: Consistency relationships. Edge weight: fraction of consistent output of all outputs generated from same data items 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 9
  • 12. Consistency Clique Complete subgraph of an attestation graph which has 2 or more nodes All nodes always agree with each other (i.e., all edge weights are 1) 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 10 2 1 3 5 4
  • 13. How to find malicious nodes Intuitions Honest nodes will always agree with each other to produce the same outputs, given the same data Number of malicious nodes is less than half of all nodes 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 11
  • 14. Finding Consistency Cliques: BK Algorithm Goal: find the maximal clique in the attestation graph Technique: Apply Bron-Kerbosch algorithm to find the maximal clique(s) (see better example at Wikipedia) Any node not in a maximal clique of size k/2 is a malicious node 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 12 Note: BK algorithm is NP-Hard Authors proposed 2 optimizations to make it run quicker
  • 15. Identifying attack patterns 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 13 NCAM PTFC/NCPM PTFC FTFC
  • 16. Inferring data quality Quality = 1 – (c/n) where n = total number of unique data items c = total number of duplicated data with inconsistent results 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 14
  • 17. Evaluation Extended IBM System S Experiments: Detection rate Sensitivity to parameters Comparison with majority voting 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 15
  • 18. Evaluation 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 16 NCPM (b=0.2, c=0) Different misbehavior probabilities
  • 19. Discussion Threat model High cost of Bron-Kerbosch algorithm (O(3n/3)) Results are for building attestation graphs per function Scalability Experimental evaluation 3/22/2010 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan 17
  • 20. 3/22/2010 18 en.600.412 Spring 2010 Lecture 6 | JHU | Ragib Hasan Further Reading TPM Reset Attack http://www.cs.dartmouth.edu/~pkilab/sparks/ Halderman et al., Lest We Remember: Cold Boot Attacks on Encryption Keys, USENIX Security 08, http://citp.princeton.edu/memory/