A Blockchain Based Distributed Storage System for Knowledge Graph Security

Wang, Yichuan; Yin, Xinyue; Zhu, He; Hei, Xinhong

doi:10.1007/978-3-030-57881-7_29

Cited by 12 publications

(11 citation statements)

References 12 publications

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“…While these results are of great interest in the context of an AI marketplace, distributed data storage has attracted much broader interest [44]- [59]. Reading and writing data to decentralized storage is relevant to, for example, the semantic web [44], supply chains [46], and knowledge graphs [57]. To our knowledge, this is the most comprehensive evaluation of multiple storage technologies with results relevant beyond AI-to-AI communication.…”

Section: Discussionmentioning

confidence: 99%

“…Beyond the limited scope, the particular choice of blockchain makes this study less relevant as a storage alternative due to Ethereum's unpredictable and sometimes high fees and congestion. [60]- [62] Wang et al [57] investigate knowledge graph storage on IPFS with the file index hashes stored and made accessible on Hyperledger Fabric. They present experimental results of write times using Hyperledge Fabric with four peer nodes running on a single machine.…”

Section: Related Workmentioning

confidence: 99%

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An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace

Tara,

Turesson,

Natea

et al. 2023

IEEE Access

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Given the exploding interest in generative AI and the concern that a few companies like Microsoft will monopolize access to such models, we address this centralization risk in the context of a DApp that matches buyers and sellers of various AI services. A key question for a decentralized marketplace is where and how to store the metadata that specifies the services' properties in human and machine-readable formats. Having one or a few actors controlling access to that data constitutes undesirable centralization. We explore data storage alternatives to ensure decentralization, equitable match-making, and efficiency. Classifying decentralized storage alternatives as simple peer-to-peer replication, replication governed by a permissionless consensus, and replication governed by a private consensus, we select an exemplar for each category: IPFS, Tendermint Cosmos and Hyperledger Fabric. We conduct experiments on performance and find that read and write speeds are fastest for IPFS, about two times slower for Tendermint and slowest for Hyperledger. Writing using IPFS and Tendermint takes significantly longer than reading, and finally, specifically with IPFS, write speeds strongly depend on configuration. Given these results and the properties of the storage technologies, we conclude that simple peer-to-peer storage is the best option for the proposed AI marketplace.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace

Tara,

Turesson,

Natea

et al. 2023

IEEE Access

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“…Furthermore, the attack thread correlation analysis process relies on the existing alerts to query the associated alerts, CVE items, and CAPEC items, which could be conducive to predicting the real purpose of attackers. In the authors' thesis [120], rebuilding the scene of a series of alerts based on KG was introduced in detail. The author conducts an experiment on the DARPA 2000 dataset to assess the performance of the proposed framework by comparing the number of remaining alerts after correlation analysis.…”

Section: Security Alert or Event Correlation Analysismentioning

confidence: 99%

Recent Progress of Using Knowledge Graph for Cybersecurity

et al. 2022

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In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

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“…In practice, the perturbation can increase the data size and the computation when extracting the original data, therefore the differential privacy is not always efficient. The novel blockchain technology which is a decentralized, distributed and secure database, has been used to tackle many issues of security, privacy and trust in KG [1,2]. Blockchain has been recently integrated to protect and keep the privacy of the personal data [3,4,5] which can be represented by knowledge graphs.…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches have integrated KG to enhance BC data querying and its reasoning capabilities such as [12], [13] and [14] but a few works like [15] have used BC with KG to keep data sharing privacy and security. In regards to keep only the KG privacy, [1] has proposed to merge BC with IPFS to keep only the KG integrity by saving the data hash on the BC without any access control. Among the shortcomings that are noticed by recent works do not give a complete KG privacy protection, and marginalize the access control on its data, which complicates the sharing of its content.…”

Section: Introductionmentioning

confidence: 99%

PrivyKG: Security and Privacy Preservation of Knowledge Graphs Using BlockChain Technology

Djeddai,

Khemaissia

2023

IJCAI

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Recently, knowledge graph has proved its benefits in AI applications and especially in knowledge representation and reasoning. Therefore, with the huge amount of published data, knowledge graph privacy, trust and integrity take an important role to protect it from illegal access and modification. In this paper, we propose PrivyKG, a new framework which uses the blockchain technology along with an access control based on it to preserve the knowledge graph privacy, integrity, and ensure security and trust. The proposed work has two essential parts to build a decentralized knowledge graph, where the first one uses off-chain storage for ensuring the blockchain scalability and the second one integrates a permissioned Hyperledger Fabric powered with GO smart contracts. The proposed framework is evaluated with a DBpedia dataset that illustrates the efficiency and the feasibility of our proposed against potential threats. Povzetek: Predstavljena je metoda decentraliziranih grafov znanja za bločne aplikacije.

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A Blockchain Based Distributed Storage System for Knowledge Graph Security

Cited by 12 publications

References 12 publications

An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace

An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace

Recent Progress of Using Knowledge Graph for Cybersecurity

PrivyKG: Security and Privacy Preservation of Knowledge Graphs Using BlockChain Technology

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