DistProv-Data Provenance in Distributed Cloud for Secure Transfer of Digital Assets with Ethereum Blockchain using ZKP

The importance and usage of the distributed cloud is increasing rapidly over a traditionally centralized cloud for the storing and exchanging of digital assets between untrusted parties in many business sectors. Storing the digital assets in the distributed cloud is considered superior to traditional cloud computing in terms of environmentally friendly, cost, security and other technical dimensions. In this article, a contemporary architecture DistProv is proposed where an open source distributed cloud IPFS is used to store and transfer the digital assets between the consignor and consignee. These two are untrusted parties exchanging sensitive documents secured by cryptographic algorithms with permission-based access verified by ethereum smart contracts using zero-knowledge proof (ZKP) and simultaneously publishing the provenance data about the digital asset as a transaction on the blockchain. This article also discusses on verifying the integrity of the digital assets and authentication of the consignor and thus preserving a strong CIA triad.

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CoPS - Cooperative Provenance System with ZKP using Ethereum Blockchain Smart Contracts

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2018100103 ◽

2018 ◽

Vol 9 (4) ◽

pp. 40-53 ◽

Cited By ~ 3

Author(s):

Navya Gouru ◽

NagaLakshmi Vadlamani

Keyword(s):

Cloud Storage ◽

Distributed Database ◽

Data Provenance ◽

Smart Contracts ◽

Zero Knowledge ◽

Provenance Management ◽

Monetary Compensation ◽

Provenance Data ◽

Secured Data ◽

Contemporary Architecture

The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the traditional cloud has economized with cooperative cloud storage like Storj and Sia, decentralized storage, which allows renting the unused hard drive space and getting monetary compensation in an exchange with cryptocurrency. In this article, the authors explain how confidentiality, integrity and availability can be progressed with cooperative cloud storage along with tamper-proof data provenance management with ethereum smart contracts using zero-knowledge proof (ZKP). A contemporary architecture is proposed with regards to storing data on the cooperative cloud and collecting and verifying the provenance data from the cloud and publishing the provenance data into blockchain network as transactions.

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CoPS - Cooperative Provenance System with ZKP using Ethereum Blockchain Smart Contracts

Research Anthology on Blockchain Technology in Business, Healthcare, Education, and Government ◽

10.4018/978-1-7998-5351-0.ch032 ◽

2021 ◽

pp. 572-586

Author(s):

Navya Gouru ◽

NagaLakshmi Vadlamani

Keyword(s):

Cloud Storage ◽

Distributed Database ◽

Data Provenance ◽

Smart Contracts ◽

Zero Knowledge ◽

Hard Drive ◽

Provenance Management ◽

Provenance Data ◽

Secured Data ◽

Contemporary Architecture

The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the traditional cloud has economized with cooperative cloud storage like Storj and Sia, decentralized storage, which allows renting the unused hard drive space and getting monetary compensation in an exchange with cryptocurrency. In this article, the authors explain how confidentiality, integrity and availability can be progressed with cooperative cloud storage along with tamper-proof data provenance management with ethereum smart contracts using zero-knowledge proof (ZKP). A contemporary architecture is proposed with regards to storing data on the cooperative cloud and collecting and verifying the provenance data from the cloud and publishing the provenance data into blockchain network as transactions.

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Trade or Trick?

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3491051 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-26

Author(s):

Pengcheng Xia ◽

Haoyu Wang ◽

Bingyu Gao ◽

Weihang Su ◽

Zhou Yu ◽

...

Keyword(s):

Machine Learning ◽

Security And Privacy ◽

Smart Contracts ◽

Early Stages ◽

Asset Trading ◽

Digital Asset ◽

Guilt By Association ◽

Privacy Issues ◽

Digital Assets

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.

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Bioinformatics Workflows With NoSQL Database in Cloud Computing

Evolutionary Bioinformatics ◽

10.1177/1176934319889974 ◽

2019 ◽

Vol 15 ◽

pp. 117693431988997

Author(s):

Polyane Wercelens ◽

Waldeyr da Silva ◽

Fernanda Hondo ◽

Klayton Castro ◽

Maria Emília Walter ◽

...

Keyword(s):

Cloud Computing ◽

Molecular Biology ◽

Database Systems ◽

Data Provenance ◽

Cloud Computing Environment ◽

Computational Environment ◽

Provenance Data ◽

Nosql Database ◽

Technical Details ◽

Bioinformatics Workflows

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

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ACM SIGMETRICS International Workshop on Distributed Cloud Computing (DCC)

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3439602.3439617 ◽

2020 ◽

Vol 48 (2) ◽

pp. 39-40

Author(s):

Andrea Araldo ◽

Ivona Brandic ◽

Stefan Schmid

Keyword(s):

Cloud Computing ◽

International Workshop ◽

Distributed Cloud

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Session details: The 3rd Annual Workshop on Distributed Cloud Computing (DCC 2015)

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3263046 ◽

2015 ◽

Vol 43 (3) ◽

Keyword(s):

Cloud Computing ◽

Distributed Cloud

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Manipulating the digital asset market: challenges in establishing a criminal legal defense regime

Public Administration ◽

10.22394/2070-8378-2021-23-2-68-73 ◽

2021 ◽

Vol 23 (2) ◽

pp. 68-73

Author(s):

ZARINA KHISAMOVA ◽

◽

ELENA KOMOVA ◽

Keyword(s):

Criminal Law ◽

Asset Market ◽

Effective Protection ◽

Wide Availability ◽

Digital Asset ◽

Low Efficiency ◽

The Moment ◽

Legal Defense ◽

Digital Assets ◽

Selfish Interest

The growth of the digital asset ecosystem has led to the emergence of hundreds of crypto exchanges that facilitate the trade of digital assets. This phenomenon did not remain without the attention of malefactors. Today, we can talk about an independent type of “cryptocurrency exchange crime”. The article analyzes the key criminal trends and formulates the main recommendations for the prevention of criminal encroachments in the cryptocurrency market. At the moment, one of the most common manipulative schemes is “pump and dump”, which became notorious at the turn of the 90s and 2000s. To date, no jurisdiction has created an effective protection regime against such fraud. The authors highlight the main reasons for the low efficiency of the measures taken: the presence of digital assets on the Internet led to the online nature of all stages of manipulations coordinated by anonymous groups in social networks; wide availability and lack of restrictions on the placement of tokens predetermined their regular and widespread placement (as opposed to an IPO); selfish interest of crypto-exchange sites that receive a commission from a transaction. It is emphasized that regulators’ search for a balance between innovation and investor protection is driving the reluctance of many jurisdictions to introduce proper criminal law protection regimes.

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Using Distributed Cloud Computing to Solve Resource-Intensive Tasks

PROGRAMMNAYA INGENERIA ◽

10.17587/prin.12.233-245 ◽

2021 ◽

Vol 12 (5) ◽

pp. 233-254

Author(s):

D. Yu. Bulgakov ◽

Keyword(s):

Cloud Computing ◽

Point Of View ◽

Free Software ◽

Production Environment ◽

Learning Models ◽

Python Programming Language ◽

Task Processing ◽

Python Programming ◽

Distributed Cloud ◽

Machine Learning Models

A method for solving resource-intensive tasks that actively use the CPU, when the computing resources of one server become insufficient, is proposed. The need to solve this class of problems arises when using various machine learning models in a production environment, as well as in scientific research. Cloud computing allows you to organize distributed task processing on virtual servers that are easy to create, maintain, and replicate. An approach based on the use of free software implemented in the Python programming language is justified and proposed. The resulting solution is considered from the point of view of the theory of queuing. The effect of the proposed approach in solving problems of face recognition and analysis of biomedical signals is described.

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