Towards Enabling Trusted Artificial Intelligence via Blockchain

Sarpatwar, Kanthi K.; Vaculín, Roman; Min, Hyun Jin; Su, Gong; Heath, Terry; Ganapavarapu, Giridhar; Dillenberger, Donna

doi:10.1007/978-3-030-17277-0_8

Cited by 42 publications

(43 citation statements)

References 16 publications

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“…Other blockchain-based provenance work, such as ProvChain [11] and SmartProvenance [17], exceed at tracking single file changes with sophisticated privacy features but cannot trace AI assets along all phases of value chains and are therefore not addressing the abovementioned challenges. The work of Sarpatwar et al [18] focuses on enabling trusted AI for interacting value chains performing federated learning. However, as the exchange of datasets is by design not supported, it is not possible to track interacting value chains outside of the federated learning context.…”

Section: Discussionmentioning

confidence: 99%

“…This calls for further research on how to design a system that addresses all of them. In this work, we generalize the provenance model of Sarpatwar et al [18] to be able to represent interacting AI value chains of any sort. Additionally, we build a system that stores this provenance model and supports the exchange of confidential assets without the need for a centralized authority.…”

Section: Limitations Of State Of the Artmentioning

confidence: 99%

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Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

Lüthi

Gagnaux

Gygli

2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

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High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations Of State Of the Artmentioning

confidence: 99%

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

Lüthi

Gagnaux

Gygli

2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

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“…This data can describe a large share of the ML pipeline -training data origin, training data, ML model modifications, or testing data [52]. In most systems described in these articles, the data itself is not stored on the blockchain, but hashes of the data [52]. In some cases, the systems use relatively simple ML models and store plain model updates on the blockchain [53].…”

Section: ) Federated Learningmentioning

confidence: 99%

“…The DLT for XAI literature mainly covers data provenance or computational integrity aspects for model training or inference. Sarpatwar, et al [52] design a DLT-based federated learning system for trusted AI and present five requirements of blockchain for trusted AI. First, guarantees of AI model ownership and track of use are important.…”

Section: Explainable Aimentioning

confidence: 99%

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On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

et al. 2020

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Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in uncertain environments. In recent research, both technologies are used in similar and even the same systems. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major innovations in information technology. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on previous work and providing rigorously derived future research opportunities. This work helps researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies.

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Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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Towards Enabling Trusted Artificial Intelligence via Blockchain

Cited by 42 publications

References 16 publications

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

Adversarial learning techniques for security and privacy preservation: A comprehensive review

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