Collaborative network outage troubleshooting with secure multiparty computation

Djatmiko, Mentari; Schatzmann, Dominik; Dimitropoulos, Xenofontas; Friedman, Arik; Boreli, Roksana

doi:10.1109/mcom.2013.6658656

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…In other words, the modified DL models should be able to be trained using the encrypted data [90], since applications require the traffic data to be encrypted for privacy preservation. In this strand, the modification of DL models usually needs to consider the methods of traditional privacy preservation technologies such as homomorphic encryption [94], secure multi-party computation [95], and differential privacy [96]. A number of modified DL models have been developed, e.g., E2DM [85] and Gazelle [86] for the cases under homomorphic encryption, DeepSecure [87] and ABY3 [88] for secure multiparty computation, and PATE [89] for differential privacy based scenarios.…”

Section: A Privacy Preservation In Shared Resource Infrastructurementioning

confidence: 99%

Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Dai

et al. 2021

Computer Networks

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Section: A Privacy Preservation In Shared Resource Infrastructurementioning

confidence: 99%

Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Dai

et al. 2021

Computer Networks

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“…[11] applied SMC to generate network traffic statistics and anomaly detection. Similarly, [16] reused the same framework to perform collaborative outage detection. Both do not address deployment and data access challenges.…”

Section: Related Workmentioning

confidence: 99%

Data Querying and Access Control for Secure Multiparty Computation

von Maltitz,

Bitzer,

Carle

2019

Preprint

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In the Internet of Things and smart environments data, collected from distributed sensors, is typically stored and processed by a central middleware. This allows applications to query the data they need for providing further services. However, centralization of data causes several privacy threats: The middleware becomes a third party which has to be trusted, linkage and correlation of data from different context becomes possible and data subject lose control over their data. Hence, other approaches than centralized processing should be considered. Here, Secure Multiparty Computation is a promising candidate for secure and privacy-preserving computation happening close to the sources of the data. In order to make SMC fit for application in these contexts, we extend SMC to act as a service: We provide elements which allow third parties to query computed data from a group of peers performing SMC. Furthermore, we establish fine-granular access control on the level of individual data queries, yielding data protection of the computed results. By adding measures to inform data sources about requests and the usage of their data, we show how a fully privacy-preserving service can be built on the foundation of SMC.

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“…Djatmiko et al [5] reused SEPIA in 2013 to perform collaborative outage detection. They extended an existing approach to work privately with multiple inputs.…”

Section: Related Workmentioning

confidence: 99%

A management framework for secure multiparty computation in dynamic environments

Maltitz

Smarzly

Kinkelin

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Secure multiparty computation (SMC) is a promising technology for privacy-preserving collaborative computation. In the last years several feasibility studies have shown its practical applicability in different fields. However, it is recognized that administration, and management overhead of SMC solutions are still a problem [1] [2]. A vital next step is the incorporation of SMC in the emerging fields of the Internet of Things and (smart) dynamic environments. In these settings, the properties of these contexts make utilization of SMC even more challenging since some vital premises for its application regarding environmental stability and preliminary configuration are not initially fulfilled.We bridge this gap by providing FlexSMC, a management and orchestration framework for SMC which supports the discovery of nodes, supports a trust establishment between them and realizes robustness of SMC session by handling nodes failures and communication interruptions. The practical evaluation of FlexSMC shows that it enables the application of SMC in dynamic environments with reasonable performance penalties and computation durations allowing soft real-time and interactive use cases.

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Collaborative network outage troubleshooting with secure multiparty computation

Cited by 8 publications

References 21 publications

Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Data Querying and Access Control for Secure Multiparty Computation

A management framework for secure multiparty computation in dynamic environments

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