Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment
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Summary
Two possible schemes: the Shamir's Secret Sharing (SSS) based on polynomial interpolation on prime fields and the Globally-Constrained Randomization (GCR) scheme based on simple blinding are considered, which show that additions via GCR are faster than via SSS, and that the relative performance gain increases when scaling up the data volume and/or number of participants, and when network conditions get worse.
- Type
- article
- Published
- 2013-12-01
- Cited by
- 2
- References
- 24
- Access
- Open access
- OpenAlex
- https://openalex.org/W1970982035
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17585502
Keywords
Computer science, Scalability, Liveness, Context (archaeology), Overhead (engineering)
References
- SEPIA: Privacy-Preserving Aggregation of Multi-Domain Network Events and Statistics
- Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Short Paper)
- Privacy-Preserving Data Mining: Models and Algorithms
- Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)
- P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users
- Comparison of CORE network emulation platforms
- FairplayMP: a system for secure multi-party computation
- Integration of the CORE and EMANE Network Emulators
- How to play ANY mental game
- Protocols for secure computations
- Privacy-preserving performance measurements
- Secure distributed data-mining and its application to large-scale network measurements
- Trapping botnets by DNS failure graphs: Validation, extension and application to a 3G network
- Privacy-preserving distributed network troubleshooting—bridging the gap between theory and practice
- A Distribution-Based Approach to Anomaly Detection and Application to 3G Mobile Traffic
- Private collaborative forecasting and benchmarking
- The devil and packet trace anonymization
- The role of network trace anonymization under attack
- Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization
- Sharemind: A Framework for Fast Privacy-Preserving Computations
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