A Performance and Resource Consumption Assessment of Secret Sharing Based Secure Multiparty Computation

Maltitz, Marcel von; Carle, Georg

doi:10.1007/978-3-030-00305-0_25

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Our testbed offers researchers a high degree of freedom to design and execute experiments, with user-controlled scripts and root access on the experiment nodes. We used pos in the past for entirely different experiments: distributed network experiments involving 15 nodes [34], low-latency analysis of single network nodes [18], and as a mere development environment for packet processing software [16]. As a testbed, the capabilities of pos neither stand out nor back compared with its main competitors [9,20,28].…”

Section: Testbed Vs Methodologymentioning

confidence: 99%

The pos framework

Gallenmüller

Scholz

Stubbe

et al. 2021

Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies

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In scientific research, the independent reproduction of experimental results is the source of trust. The release of experimental artifacts enables the reproduction of results; however, additional efforts of researchers are required to prepare and document their experiments accordingly. To honor this increased effort, multiple initiatives were implemented to incentivize the creation and release of experimental artifacts, e.g., awards for papers that provide experimental artifacts.In this work, we want to propose a novel approach toward a reproducible research-a structured experimental workflow that allows the creation of reproducible experiments without requiring additional efforts of the researcher. Moreover, we present our own testbed and toolchain, namely, plain orchestrating service (pos), which enables the creation of such experimental workflows. The experiment is documented by our proposed, fully scripted experiment structure. Further, we consider the entire experimental workflow from experiment orchestration, to data measurement, to result evaluation. In addition, pos provides scripts that enable the automation of the bundling and release of all the created experimental artifacts. In this case study, we release one of our own experiments together with the necessary tools so that others can reproduce our experiment. Additionally, we provide an interactive environment where pos experiments can be executed and reproduced, which is available at https://gallenmu.github.io/pos-artifacts. CCS CONCEPTS• Networks → Network experimentation; • General and reference → Experimentation.

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Section: Testbed Vs Methodologymentioning

confidence: 99%

The pos framework

Gallenmüller

Scholz

Stubbe

et al. 2021

Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies

Self Cite

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“…This allows to measure the overhead of our components without depending on the performance characteristics of a chosen SMC implementation. We decided for this omission, since we assessed the performance of a state of the art SMC implementation elsewhere [22].…”

Section: Methodsmentioning

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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“…While many single purpose SMPC protocols were proposed, the main interest was in the creation of a general purpose framework which allows the computation of arbitrary functions. Basic concepts were identified which allowed approaching this aim, most notably garbled circuits, homomorphic encryption and secret sharing schemes [29,90]. Its theory flourished early in the 80âĂŹs while implementations have only been developed in the last decade.…”

Section: Secure Multi-party Computationmentioning

confidence: 99%

Review of Techniques for Privacy-Preserving Blockchain Systems

Satybaldy

Nowostawski

2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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Privacy plays a central role in many application domains that utilize blockchain technology. It is central in Personal Data Management, Electronic Health records, or systems that interact with any public institution. However, blockchains are subject to potential privacy issues such as transaction linkability, compliance with data protection regulations, on-chain data privacy, and malicious smart contracts. To deal with these challenges, novel privacy-preserving solutions based on crypto-privacy techniques are emerging. The goal of this survey is to provide insights into the privacy-preserving techniques associated with blockchain. We analyse the existing privacypreserving mechanisms for blockchain and propose a framework that categorizes the main techniques examined. Furthermore, we summarize some typical applications of blockchain where the privacy protection is the main requirement. CCS CONCEPTS • General and reference → Surveys and overviews; Evaluation; • Security and privacy → Cryptography; Privacy-preserving protocols; Cryptanalysis and other attacks; • Software and its engineering → Peer-to-peer architectures; • Social and professional topics → Identity theft.

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A Performance and Resource Consumption Assessment of Secret Sharing Based Secure Multiparty Computation

Cited by 6 publications

References 28 publications

The pos framework

The pos framework

Data Querying and Access Control for Secure Multiparty Computation

Review of Techniques for Privacy-Preserving Blockchain Systems

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