Privacy-preserving delegable authentication in the internet of things

Gritti, Clémentine; Önen, Melek; Molva, Refik

doi:10.1145/3297280.3297365

Cited by 15 publications

(8 citation statements)

References 25 publications

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“…For IoT devices, attribute-based signatures offer the required flexibility, including the use of these attributes to verify properties during data collection (e.g., data accuracy). Gritti et al Gritti et al (2019) introduce such a lightweight method. Previous work Przytarski et al (2021) shows how this method can be combined with blockchain technology.…”

Section: Situation Logmentioning

confidence: 99%

Situation-aware adaptation of choreographies—The DiStOPT approach

Hirmer

Breitenbücher²,

Gaudio³

et al. 2022

Front. Internet Things

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The rise of the IoT and Industry 4.0 has increased the complexity of collaborating business processes, i. e., choreographies, as more partners and assets are involved. However, maintaining and executing business choreographies are complex tasks. Moreover, enabling robust and reliable execution is important, as failures or delays cause high costs among partners. For example, manufacturing companies usually depend on different suppliers, and it is crucial to be up-to-date about possible delays in shipments as this leads to delays in the manufacturing of their products. In this case, a choreography needs to be designed and operated in a way that it can adapt to cope with such problems. This requires i) timely recognition and tamper-resistent logging of problems that occur at each involved partner, which are referred to as situations in the scope of this article, and ii) an approach for a timely adaptation of choreographies based on occurring situations. Therefore, in this article, we introduce DiStOPT, an approach to i) model and recognize situations in a distributed and timely manner, and ii) model and execute situation-aware choreographies based on the recognized situations. The contributions are evaluated in a manufacturing scenario and validated by a prototypical implementation.

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Section: Situation Logmentioning

confidence: 99%

Situation-aware adaptation of choreographies—The DiStOPT approach

Hirmer

Breitenbücher²,

Gaudio³

et al. 2022

Front. Internet Things

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“…First, a user has to be authenticated. We apply a method from Gritti et al [16] that is similar to the attribute-based credentials process described above. To this end, a user transmits all identifying attributes to a trustworthy IoT gateway ➊.…”

Section: Pattern-based Access Controlmentioning

confidence: 99%

Trustworthy, Secure, and Privacy-aware Food Monitoring Enabled by Blockchains and the IoT

Stach

Gritti

Przytarski

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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A large number of food scandals (e. g., falsely declared meat or non-compliance with hygiene regulations) are causing considerable concern to consumers. Although Internet of Things (IoT) technologies are used in the food industry to monitor production (e. g., for tracing the origin of meat or monitoring cold chains), the gathered data are not used to provide full transparency to the consumer. To achieve this, however, three aspects must be considered: a) The origin of the data must be verifiable, i. e., it must be ensured that the data originate from calibrated sensors. b) The data must be stored tamper-resistant, immutable, and open to all consumers. c) Despite this openness, the privacy of affected data subjects (e. g., the carriers) must still be protected. To this end, we introduce the SHEEPDOG architecture that "shepherds" products from production to purchase to enable a trustworthy, secure, and privacy-aware food monitoring. In SHEEPDOG, attribute-based credentials ensure trustworthy data acquisition, blockchain technologies provide secure data storage, and finegrained access control enables privacy-aware data provision.

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“…As IoT technologies capture growing volumes of time series data, there is an emerging need to comprehensively analyze it in an efficient manner. While there are approaches to verify the authenticity of the sources of this IoT data [120] and subsequently provide these time series data to third parties on a demand-driven basis [121], it is also necessary to ensure that the data cannot be tampered with when it is stored and managed.…”

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confidence: 99%

Query Processing in Blockchain Systems: Current State and Future Challenges

et al. 2021

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When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient—comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.

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Privacy-preserving delegable authentication in the internet of things

Cited by 15 publications

References 25 publications

Situation-aware adaptation of choreographies—The DiStOPT approach

Situation-aware adaptation of choreographies—The DiStOPT approach

Trustworthy, Secure, and Privacy-aware Food Monitoring Enabled by Blockchains and the IoT

Query Processing in Blockchain Systems: Current State and Future Challenges

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