ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability

Liang, Xueping; Shetty, Sachin; Tosh, Deepak K.; Kamhoua, Charles A.; Kwiat, Kevin; Njilla, Laurent

doi:10.1109/ccgrid.2017.8

Cited by 578 publications

(276 citation statements)

References 15 publications

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“…Removing the centralized party with a decentralized blockchain increases trust among the participants and encourages collaboration. 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.…”

Section: Discussionmentioning

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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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%

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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“…In this manner, any malicious falsifications of data can be captured. In addition, Liang et al [116] proposed ProvChain to collect and verify cloud data provenance. The main idea of ProvChain is to embed provenance data into blockchain transactions so that any data modifications are traceable.…”

Section: Data Provenancementioning

confidence: 99%

An overview on smart contracts: Challenges, advances and platforms

Zheng

Xie

Dai

et al. 2020

Future Generation Computer Systems

731

245

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Smart contract technology is reshaping conventional industry and business processes. Being embedded in blockchains, smart contracts enable the contractual terms of an agreement to be enforced automatically without the intervention of a trusted third party. As a result, smart contracts can cut down administration and save services costs, improve the efficiency of business processes and reduce the risks. Although smart contracts are promising to drive the new wave of innovation in business processes, there are a number of challenges to be tackled. This paper presents a survey on smart contracts. We first introduce blockchains and smart contracts. We then present the challenges in smart contracts as well as recent technical advances. We also compare typical smart contract platforms and give a categorization of smart contract applications along with some representative examples.

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“…It provides a shared and distributed architecture [36][37][38][39]. Based on the concept, the hashed data of raw data (uploaded data by platform users) can be generated and transferred to the open data governance.…”

Section: Technical Considerationsmentioning

confidence: 99%

“…Based on the mechanisms, Zyskind et al [39] proposed a decentralized personal data management system to achieve a transparent data supply chain, data ownership and privacy. The data provenance issue is addressed in Liang et al [36] through a provenance database and a blockchain network to provide temperproof records for transparency of data accountability. These studies, however, overlook the complicated relationships and context which can occur in PEs through multiple participating groups, various types of data and a complex data supply chain.…”

Section: Literature Reviewmentioning

confidence: 99%