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

2018 ◽  
Vol 9 (4) ◽  
pp. 40-53 ◽  
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.

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

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.

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

2019 ◽  
Vol 10 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Navya Gouru ◽  
NagaLakshmi Vadlamani
Keyword(s):  
Cloud Computing ◽  
Data Provenance ◽  
Smart Contracts ◽  
Provenance Data ◽  
Cryptographic Algorithms ◽  
Digital Asset ◽  
Contemporary Architecture ◽  
Distributed Cloud ◽  
Business Sectors ◽  
Digital Assets

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.

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

2020 ◽  
pp. 866-890
Author(s):  
Navya Gouru ◽  
NagaLakshmi Vadlamani
Keyword(s):  
Cloud Computing ◽  
Data Provenance ◽  
Smart Contracts ◽  
Provenance Data ◽  
Cryptographic Algorithms ◽  
Digital Asset ◽  
Contemporary Architecture ◽  
Distributed Cloud ◽  
Business Sectors ◽  
Digital Assets

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.

scholarly journals PROV‐man: A PROV‐compliant toolkit for provenance management

2015 ◽  
Author(s):  
Ammar Benabdelkader ◽  
Antoine A.H.C. van Kampen ◽  
Silvia D Olabarriaga
Keyword(s):  
Data Analysis ◽  
Scientific Practice ◽  
Modern Science ◽  
Open Architecture ◽  
Complex Case ◽  
Data Provenance ◽  
Heterogeneous Environments ◽  
Biomedical Data ◽  
Provenance Management ◽  
Provenance Data

Discoveries in modern science can take years and involve the contribution of large amounts of data, many people and various tools. Although good scientific practice dictates that findings should be reproducible, in practice there are very few automated tools that actually support traceability of the scientific method employed, in particular when various experimental environments are involved at different research phases. Data provenance tracking approaches can play a major role in addressing many of these challenges. These approaches propose ways to capture, manage, and use of provenance information to support the traceability of the scientific methods in heterogeneous environments. PROV is a W3C standard that provides a comprensive model for data and semantics representation with common vocabularies and rich concepts to describe provenance. Nevertheless, it is difficult for domain scientists to easily understand and adopt all the richeness provided by PROV. In this paper we describe the design and implementation of the provenance manager PROV-man, a PROV-compliant framework that facilitates the tasks of scientists in integrating provenance capabilities into their data analysis tools. PROV-man provides functionalities to create and manipulate provenance data in a consistent manner and ensures its permanent storage. It also provides a set of interfaces to serialize and export provenance data into various data formats, serving interoperability. The open architecture of PROV-man, consisting of an API and a configurable database, allows for its easy deployment within existing and newly developed software tools. The paper presents examples illustrating the usage of PROV-man. The first example illustrates how to create and manipulate provenance data of an online newspaper article using PROV-man. The second example demonstrates and evaluates the PROV-man implementation in a more complex case for collection of provenance data about biomedical data analysis activities that are carried out using a distributed computing infrastructure.

scholarly journals PROV‐man: A PROV‐compliant toolkit for provenance management

2015 ◽  
Author(s):  
Ammar Benabdelkader ◽  
Antoine A.H.C. van Kampen ◽  
Silvia D Olabarriaga
Keyword(s):  
Data Analysis ◽  
Scientific Practice ◽  
Modern Science ◽  
Open Architecture ◽  
Complex Case ◽  
Data Provenance ◽  
Heterogeneous Environments ◽  
Biomedical Data ◽  
Provenance Management ◽  
Provenance Data

Discoveries in modern science can take years and involve the contribution of large amounts of data, many people and various tools. Although good scientific practice dictates that findings should be reproducible, in practice there are very few automated tools that actually support traceability of the scientific method employed, in particular when various experimental environments are involved at different research phases. Data provenance tracking approaches can play a major role in addressing many of these challenges. These approaches propose ways to capture, manage, and use of provenance information to support the traceability of the scientific methods in heterogeneous environments. PROV is a W3C standard that provides a comprensive model for data and semantics representation with common vocabularies and rich concepts to describe provenance. Nevertheless, it is difficult for domain scientists to easily understand and adopt all the richeness provided by PROV. In this paper we describe the design and implementation of the provenance manager PROV-man, a PROV-compliant framework that facilitates the tasks of scientists in integrating provenance capabilities into their data analysis tools. PROV-man provides functionalities to create and manipulate provenance data in a consistent manner and ensures its permanent storage. It also provides a set of interfaces to serialize and export provenance data into various data formats, serving interoperability. The open architecture of PROV-man, consisting of an API and a configurable database, allows for its easy deployment within existing and newly developed software tools. The paper presents examples illustrating the usage of PROV-man. The first example illustrates how to create and manipulate provenance data of an online newspaper article using PROV-man. The second example demonstrates and evaluates the PROV-man implementation in a more complex case for collection of provenance data about biomedical data analysis activities that are carried out using a distributed computing infrastructure.

An integrity verification scheme of completeness and zero-knowledge for multi-Cloud storage

2017 ◽  
Vol 30 (16) ◽  
pp. e3324 ◽  
Author(s):  
Laicheng Cao ◽  
Wenwen He ◽  
Yufei Liu ◽  
Xian Guo ◽  
Tao Feng
Keyword(s):  
Cloud Storage ◽  
Zero Knowledge ◽  
Verification Scheme ◽  
Integrity Verification ◽  
Multi Cloud

A novel approach to provenance management for privacy preservation

2019 ◽  
Vol 46 (2) ◽  
pp. 147-160
Author(s):  
Ozgu Can ◽  
Dilek Yilmazer
Keyword(s):  
Privacy Preservation ◽  
Quality Of Data ◽  
Semantic Web Technologies ◽  
Provenance Information ◽  
Web Technologies ◽  
Provenance Management ◽  
Novel Approach ◽  
Provenance Data ◽  
Domain Independent

Provenance determines the origin of the data by tracing and recording the actions that are performed on the data. Therefore, provenance is used in many fields to ensure the reliability and quality of data. In this work, provenance information is used to meet the security needs in information systems. For this purpose, a domain-independent provenance model is proposed. The proposed provenance model is based on the Open Provenance Model and Semantic Web technologies. The goal of the proposed provenance model is to integrate the provenance and security concepts in order to detect privacy violations by querying the provenance data. In order to evaluate the proposed provenance model, we illustrated our domain-independent model by integrating it with an infectious disease domain and implemented the Healthcare Provenance Information System.

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