Digital Resource Rights Confirmation and Infringement Tracking Based on Smart Contracts

Qian, Peng; Liu, Zhenguang; Wang, Xun; Jianhai, Chen; Wang, Bei; Zimmermann, Roger

doi:10.1109/ccis48116.2019.9073733

Cited by 13 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The program maintains the logical flow of contract clauses by preserving their interconnectedness. The recording of each contract statement's operation is documented as an unalterable transaction that is preserved on the blockchain [31]. Smart contracts ensure correct access management and?execution.…”

Section: Smart Contractmentioning

confidence: 99%

Blockchain-based PDF File Copyright Protection and Tracing

Gao,

Wan,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

As network technology advances at a rapid pace, an increasing number of individuals are utilizing the Internet to transmit and receive information. However, copying, altering, and other malicious acts constantly threaten the copyright ownership of electronic documents in this open environment of the Internet. Numerous copyright theft, illegal transmission, information forgery, and other problems are emerging. Moreover, because of the absence of copyright protection methods, PDF file owners often need help to secure the subsequent legal use of their PDF files after uploading them to the Internet. Combining blockchain and data hiding of PDF files for the first time, we propose a blockchain-based copyright protection scheme for PDF files to uniformly handle the problems of copyright authentication, authenticity identification, and copyright disputes of PDF files. First, we propose a new information hiding algorithm based on the incremental update characteristics of PDF, which embeds copyright information in PDF files. Second, the scheme enables users to establish their access control policies via smart contracts and delivers on-chain PDF file proofs, which can be employed to validate the ownership rights of PDF files and track the transaction history of PDF files. We have implemented the system on the Ethereum platform and analyzed its security and traceability. The experimental results demonstrate that the proposed information hiding algorithm possesses robust security and that the copyright registration method is rapid

show abstract

Section: Smart Contractmentioning

confidence: 99%

Blockchain-based PDF File Copyright Protection and Tracing

Gao,

Wan,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Over recent years, virtual currencies and blockchain technologies have emerged as powerful catalysts of social change and financial value creation. Blockchain technology [2][3][4][5] has witnessed substantial advancements, finding applications in diverse domains such as healthcare [6][7][8][9][10], Internet of Things (IoT) services [11][12][13][14][15], supply chain management [16][17][18][19][20], copyright protection [21][22][23][24][25], and energy internet [26][27][28][29][30]. Of noteworthy mention is Ether [31], the presently most prevalent blockchain platform.…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

Wu,

Peng,

et al. 2024

CMES

View full text Add to dashboard Cite

In recent years, the number of smart contracts deployed on blockchain has exploded. However, the issue of vulnerability has caused incalculable losses. Due to the irreversible and immutability of smart contracts, vulnerability detection has become particularly important. With the popular use of neural network model, there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts. This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts. Subsequently, it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection. These tools are categorized based on their open-source status, the data format and the type of feature extraction they employ. Then we conduct a comprehensive comparative analysis of these tools, selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy. Finally, Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools, we suppose to provide a reference standard for developers of contract vulnerability detection tools. Meanwhile, forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

show abstract

“…Due to the immutable nature of blockchain, once a smart contract is deployed on the blockchain, its defined rules will be strictly followed during execution. Smart contracts make the automatic execution of contract terms possible, giving rise to a variety of decentralized applications [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting

Liu¹,

Qian

Yang

et al. 2023

IEEE Trans.Inform.Forensic Secur.

Self Cite

View full text Add to dashboard Cite

Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Smart contracts from various fields now hold over one trillion dollars worth of virtual coins, attracting numerous attacks. Quite a few practitioners have devoted themselves to developing tools for detecting bugs in smart contracts. One line of efforts revolve around static analysis techniques, which heavily suffer from high false positive rates. Another line of works concentrate on fuzzing techniques. Unfortunately, current fuzzing approaches for smart contracts tend to conduct fuzzing starting from the initial state of the contract, which expends too much energy revolving around the initial state of the contract and thus is usually unable to unearth bugs triggered by other states. Moreover, most existing methods treat each branch equally, failing to take care of the branches that are rare or more likely to possess bugs. This might lead to resources wasted on normal branches.In this paper, we try to tackle these challenges from three aspects: (1) In generating function invocation sequences, we explicitly consider data dependencies between functions to facilitate exploring richer states. We further prolong a function invocation sequence S1 by appending a new sequence S2, so that the appended sequence S2 can start fuzzing from states that are different from the initial state. (2) We incorporate a branch distance-based measure to evolve test cases iteratively towards a target branch. (3) We engage a branch search algorithm to discover rare and vulnerable branches, and design an energy allocation mechanism to take care of exercising these crucial branches. We implement IR-Fuzz and extensively evaluate it over 12K real-world contracts. Empirical results show that: (i) IR-Fuzz achieves 28% higher branch coverage than state-of-theart fuzzing approaches, (ii) IR-Fuzz detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods, and (iii) IR-Fuzz is fast, generating an average of 350 test cases per second. Our implementation and dataset are released at https://github.com/Messi-Q/IR-Fuzz, hoping to facilitate future research.

show abstract

Digital Resource Rights Confirmation and Infringement Tracking Based on Smart Contracts

Cited by 13 publications

References 9 publications

Blockchain-based PDF File Copyright Protection and Tracing

Blockchain-based PDF File Copyright Protection and Tracing

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting

Contact Info

Product

Resources

About