SEPIA: Privacy-Preserving Aggregation of Multi-Domain Network Events and Statistics
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Summary
This paper designs privacy-preserving protocols for event correlation and aggregation of network traffic statistics, such as addition of volume metrics, computation of feature entropy, and distinct item count, and evaluates the running time and bandwidth requirements of these protocols in realistic settings on a local cluster as well as on PlanetLab.
- Type
- article
- Published
- 2010-08-11
- Cited by
- 367
- References
- 55
- OpenAlex
- https://openalex.org/W25045116
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7601427
Keywords
Computer science, Computation, PlanetLab, Secure multi-party computation, Anomaly detection
References
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- Toward a Framework for Internet Forensic Analysis
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- Privacy-Preserving Sharing and Correlation of Security Alerts
- Privacy-preserving collaborative anomaly detection
- Global Intrusion Detection in the DOMINO Overlay System
- Worm and Attack Early Warning
- Security against probe-response attacks in collaborative intrusion detection
- Fast Privacy-Preserving Top-k Queries Using Secret Sharing
- FairplayMP: a system for secure multi-party computation
- Completeness theorems for non-cryptographic fault-tolerant distributed computation
- How to play ANY mental game
- Simplified VSS and fast-track multiparty computations with applications to threshold cryptography
- Large-scale collection and sanitization of network security data: risks and challenges
- Protocols for secure computations
- Preface to Special Issue: Theory and Applications of Models of Computation (TAMC)
- Community-oriented network measurement infrastructure (CONMI) workshop report
- Sharing computer network logs for security and privacy: a motivation for new methodologies of anonymization
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- Enkla statistiska undersökningar med VIFF
- Secure outsourced computation of iris matching
- A Secure Multi-Party Computation Protocol Suite Inspired by Shamir's Secret Sharing Scheme
- Rational Multiparty Computation
- Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Short Paper)
- Privacy-Friendly Collaboration for Cyber Threat Mitigation
- Comindis: collaborative monitoring with minimum disclosure
- Design and Implementation of a Secure Auction System for Air Transport Slots
- Detection, Classification and Visualization of Anomalies using Generalized Entropy Metrics
- Multi-cloud privacy preserving schemes for linear data mining
- Choose Wisely: A Comparison of Secure Two-Party Computation Frameworks
- Automatic Proofs of Privacy of Secure Multi-party Computation Protocols against Active Adversaries
- Experimental evaluation of privacy-preserving aggregation schemes on planetlab
- Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)
- Network flow problems with secure multiparty computation
- Privacy preserving distributed network outage monitoring
- Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment
- Building a decentralized, cooperative, and privacy-preserving monitoring system for trustworthiness: the approach of the EU FP7 DEMONS project [Very Large Projects]
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