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AMIS: Software-Defined Privacy- Preserving Flow Measurement Instrument and Services Yan Luo, Univ. of Massachusetts Lowell Co-PI: Cody Bumgardner, Univ.

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Presentation on theme: "AMIS: Software-Defined Privacy- Preserving Flow Measurement Instrument and Services Yan Luo, Univ. of Massachusetts Lowell Co-PI: Cody Bumgardner, Univ."— Presentation transcript:

1 AMIS: Software-Defined Privacy- Preserving Flow Measurement Instrument and Services Yan Luo, Univ. of Massachusetts Lowell Co-PI: Cody Bumgardner, Univ. of Kentucky Co-PI: Gabriel Ghinita, Univ. of Massachusetts Boston Co-PI: Michael McGarry, Univ. of Texas El Paso Yan Luo, Univ. of Massachusetts Lowell Co-PI: Cody Bumgardner, Univ. of Kentucky Co-PI: Gabriel Ghinita, Univ. of Massachusetts Boston Co-PI: Michael McGarry, Univ. of Texas El Paso

2 AMIS Project Objectives 40+Gbps flow-granularity network measurement instrument –216-core network processor + multicore x86 Software defined measurement APIs & libraries –Flexible specification of measurement targets and metrics Preserving privacy of network flow data –Workload-aware privacy protections In-depth flow analytics –Project/AS utilization, patterns and trends 2

3 The Box 3

4 Software Defined Measurement 4

5 Privacy Protection vs. Computational Complexity 5 Strongest Protection Slow Performance Limited Query Capabilities Offline Mode Only Strong Protection Moderate Performance Moderate Query Capabilities Limited Operational Mode Best-effort Protection Fast Performance Flexible Query Capabilities Supports Operational Mode Performance Differential Privacy Searchable Encryption Syntactic Privacy (k- anonymity, l-diversity) Tradeoff in privacy and computation overheads

6 Preserving Data Privacy in AMIS 6

7 Measurement Data Management and Processing Decentralized hierarchical resource management common data integration schema to be used across analytic, communication, and storage components Distributed system for high volume streaming data processing GUI, reporting tools, data repo 7

8 Network Flow Analytics Distinguish traffic matrix and traffic types –identify project association of traffic flows –file transfers, interactive sessions, short-lived Stochastic modeling –descriptive statistics –auto-correlation Derive network activity patterns and trends Provide insights to network management –What if scenarios 8

9 Test and Validation Plan 9

10 NSF I-Corps Interviews! Can I (and my student) talk with you for about 15 minutes? 10


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