AMIS: Software-Defined Privacy- Preserving Flow Measurement Instrument and Services Yan Luo, Univ....
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Transcript of AMIS: Software-Defined Privacy- Preserving Flow Measurement Instrument and Services Yan Luo, Univ....
AMIS: Software-Defined Privacy-Preserving Flow Measurement
Instrument and Services
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
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
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Privacy Protection vs. Computational Complexity
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Strongest ProtectionSlow PerformanceLimited Query CapabilitiesOffline Mode Only
Strong ProtectionModerate PerformanceModerate Query CapabilitiesLimited Operational Mode
Best-effort ProtectionFast PerformanceFlexible Query CapabilitiesSupports Operational Mode
Perfo
rman
cePri
vacy
Differential Privacy
Searchable Encryption
Syntactic Privacy (k-anonymity, l-diversity)
• Tradeoff in privacy and computation overheads
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
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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
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