Leveraging Secure Multiparty Computation in the Internet of Things

Maltitz, Marcel von; Carle, Georg

doi:10.1145/3210240.3223569

Cited by 7 publications

(5 citation statements)

References 17 publications

(10 reference statements)

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“…For example, Azad et al [30] created a machine-to-machine reputation system (M2M-REP) to evaluate the trustworthiness of autonomous machines in the Machine to Machine (M2M) through SMPC. In pursuit of privacy protection goals, Maltitz et al [31] designed a management and orchestration middleware utilizing SMPC. With the aid of fully homomorphic encryption (FHE) and SMPC, Tonyali et al [32] evaluated the performance of two open-source wireless mesh networks under smart grid advanced metering infrastructure network (SG-AMI).…”

Section: Related Workmentioning

confidence: 99%

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Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing

Han,

Ma,

Taherkordi

et al. 2024

IET Communications

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To solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy‐preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and a k‐anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc‐based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system.

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Section: Related Workmentioning

confidence: 99%

“…In pursuit of privacy protection goals, Maltitz et al. [31] designed a management and orchestration middleware utilizing SMPC. With the aid of fully homomorphic encryption (FHE) and SMPC, Tonyali et al.…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing

Han,

Ma,

Taherkordi

et al. 2024

IET Communications

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“…HE allows for computations to be performed on encrypted data without the need of having to first decrypt it. The three main types of HE are: Middleware in IoT manages communication between hardware and applications, handling data collection, storage, analysis, processing, and forwarding to consumers [5]. However, it poses security risks as a high-value target for attackers.…”

Section: B Backgroundmentioning

confidence: 99%

“…Furthermore, due to the nature of the IoT, there are additional constraints, such as limited resources, diversity of standards, and network vulnerabilities [3]. In this resource-constrained environment, challenges regarding privacy during data aggregation and transport encryption emerge [4] [5]. Traditional cryptographic algorithms encounter challenges when applied to IoT scenarios due to inherent resource limitations such as power constraints, limited battery capacity, and the need for real-time execution [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation

Haterd,

Elhajj

2024

icisna

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With the increasing influence of IoT devices in our daily lives, secure data-sharing is becoming ever more important. Sensors and other devices are communicating vast amounts of possibly unencrypted data, which poses a significant privacy concern. To tackle this problem, this research implements two Partially Homomorphic Encryption (PHE) schemes, RSA and the Paillier cryptosystem, to perform Secure Multiparty Computation (SMPC) in the resource-constrained IoT environment. The environment consists of a laptop connected to an Arduino Uno through a serial connection. The RSA-based SMPC protocol has an average completion time of 2007ms. However, due to the inability to use padding, RSA lacks semantic security. Conversely, the Paillier-based protocol is semantically secure but cannot complete the encryption due to dynamic memory issues. Even if resolved, the estimated encryption time exceeds 103.3 minutes. Despite the potential of SMPC in IoT environments for secure data handling, the results from this research suggest that directly implementing PHE schemes on Arduino is not practical based on the observed limitations.

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“…In [23] von Maltitz et al provide a vision how SMC can be applied in smart environments: The starting point are distributed sensor platforms, understood as edge devices. They represent an intermediary to low-end sensors; they collect the data created by the connected sensors, store it locally and have sufficient resources to perform local, small-sized data processing.…”

Section: Previous Work For Smc In the Internet Of Thingsmentioning

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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Leveraging Secure Multiparty Computation in the Internet of Things

Cited by 7 publications

References 17 publications

Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing

Privacy‐preserving data integration scheme in industrial robot system based on fog computing and edge computing

Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation

Data Querying and Access Control for Secure Multiparty Computation

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