A management framework for secure multiparty computation in dynamic environments

Maltitz, Marcel von; Smarzly, Stefan; Kinkelin, Holger; Carle, Georg

doi:10.1109/noms.2018.8406322

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…FlexSMC represents a management and orchestration framework for secure multi-party computations described in [22], which addresses the issue of dynamic environments such as IoT networks mixed with privacy-preserving collaborative computations. This approach focuses on the virtual centrality of SMPC nodes, serving as a single point of data contact for requests, and eventually translates them into SMPC sessions.…”

Section: Related Workmentioning

confidence: 99%

DLT Based Authentication Framework for Industrial IoT Devices

Lupașcu

Lupascu

Bica

2020

Sensors

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The latest technological progress in the industrial sector has led to a paradigm shift in manufacturing efficiency and operational cost reduction. More often than not, this cost reduction comes at the price of dismissing information security, especially when multiple stakeholders are involved and the complexity increases. As a further matter, most of the legacy systems and smart factoring processes lack a security by design approach, making them highly vulnerable to cyber-attacks. Taking into consideration the aforementioned issues, we propose an architectural framework for Industrial Internet of Things (IIoT) that provides authentication and guaranteed integrity. Our proposal properly addresses the security by design principle while combining some of the emerging technologies like Secure Multi-Party Computation (SMPC) for grounded policy rules and Distributed Ledger Technology (DLT) for an immutable and transparent registry.

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Section: Related Workmentioning

confidence: 99%

DLT Based Authentication Framework for Industrial IoT Devices

Lupașcu

Lupascu

Bica

2020

Sensors

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“…e proposed solution addresses the security leakage issues in an untrusted cloud communication architecture. In a similar work [24], the authors focus on the centrality of blockchain nodes to manage and monitor the IoT devices. Some interesting proposals for private blockchain networks are also provided in [25,26], wherein the authors created a threat model for evaluating the security protocols.…”

Section: Literature Reviewmentioning

confidence: 99%

Rogue Device Mitigation in the Internet of Things: A Blockchain-Based Access Control Approach

Javaid

Jameel

Javaid

et al. 2020

Mobile Information Systems

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Recent technological developments in wireless and sensor networks have led to a paradigm shift in interacting with everyday objects, which nurtured the concept of Internet of Things (IoT). However, low-powered nature of IoT devices generally becomes a hindrance that makes them vulnerable to a wide array of attacks. Among these, the emergence of rogue devices is quickly becoming a major security concern. Rogue devices are malicious in nature which typically execute different kinds of cyberattacks by exploiting the weaknesses of access control schemes in IoT environments. Therefore, access control is one of the crucial aspects of an IoT ecosystem that defines an entry point for a device or a user in the network. This paper investigates this issue and presents an access control scheme by integrating an IoT network with blockchain technology, thereby arguing to replace the traditional centralized IoT-server architecture with a decentralized one. The blockchain is used with smart contracts to establish a secure platform for device registration. Due to this reason, the IoT devices are first required to register themselves and access the network via contracts thereafter. Moreover, the contracts host a device registry, the access control list, to grant or deny access to devices. This allows the proposed scheme to authorize registered devices only and block unregistered ones, which facilitates the mitigation of rogue devices. To demonstrate the feasibility and improvements of the proposed scheme, security analysis along with in-depth performance evaluation are conducted, where the obtained results indicate its applicability. A case study is also formulated with a comparative analysis that confirms the superior performance of the proposed scheme for low-powered IoT systems.

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“…However, while optimizing for a certain use case, they sacrifice the ability to compute arbitrary functions and specialize on secure aggregation. Following [24], our solution is agnostic regarding the specific SMC implementation as long as it supports an arbitrary number of computing parties.…”

Section: Related Workmentioning

confidence: 99%

“…In [24] von Maltitz et al focused on the interaction between the peers and the SMC gateway of the aforementioned vision. This work addressed natural technical mismatches between the premises of secret sharing based SMC [7,31] and the characteristics of dynamic environments.…”

Section: Previous Work For Smc In the Internet Of Thingsmentioning

confidence: 99%

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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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A management framework for secure multiparty computation in dynamic environments

Cited by 9 publications

References 17 publications

DLT Based Authentication Framework for Industrial IoT Devices

DLT Based Authentication Framework for Industrial IoT Devices

Rogue Device Mitigation in the Internet of Things: A Blockchain-Based Access Control Approach

Data Querying and Access Control for Secure Multiparty Computation

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