scholarly journals Tracking and Sketching Distributed Data Provenance

2010 ◽  
Author(s):  
Tanu Malik ◽  
Ligia Nistor ◽  
Ashish Gehani
Keyword(s):  
Data Provenance ◽  
Distributed Data

Sketching Distributed Data Provenance

2013 ◽  
pp. 85-107 ◽  
Author(s):  
Tanu Malik ◽  
Ashish Gehani ◽  
Dawood Tariq ◽  
Fareed Zaffar
Keyword(s):  
Data Provenance ◽  
Distributed Data

scholarly journals IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammed A. Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Data System ◽  
Third Party ◽  
Data Provenance ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Blockchain Technology ◽  
Iot Devices

AbstractThe diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).

The bounds of the distributed data-intensive computing systems

2007 ◽  
Vol 2 (Supplement 1) ◽  
pp. 85-96 ◽  
Author(s):  
Antal Buza
Keyword(s):  
Distributed Data ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Data Intensive

Security Test by using F T M and Data Allocation Strategies on Leakage Detection

2005 ◽  
Vol 4 (2) ◽  
pp. 393-400
Author(s):  
Pallavali Radha ◽  
G. Sireesha
Keyword(s):  
Third Party ◽  
Distributed Data ◽  
Data Allocation ◽  
Security Testing ◽  
Sensitive Data ◽  
Sample Data ◽  
The One ◽  
Security Test ◽  
Data Request ◽  
Allocation Strategies

The data distributors work is to give sensitive data to a set of presumably trusted third party agents.The data i.e., sent to these third parties are available on the unauthorized places like web and or some ones systems, due to data leakage. The distributor must know the way the data was leaked from one or more agents instead of as opposed to having been independently gathered by other means. Our new proposal on data allocation strategies will improve the probability of identifying leakages along with Security attacks typically result from unintended behaviors or invalid inputs.  Due to too many invalid inputs in the real world programs is labor intensive about security testing.The most desirable thing is to automate or partially automate security-testing process. In this paper we represented Predicate/ Transition nets approach for security tests automated generationby using formal threat models to detect the agents using allocation strategies without modifying the original data.The guilty agent is the one who leaks the distributed data. To detect guilty agents more effectively the idea is to distribute the data intelligently to agents based on sample data request and explicit data request. The fake object implementation algorithms will improve the distributor chance of detecting guilty agents.

Activity of public control entities and development of distributed computing and distributed data storage systems

2018 ◽  
pp. 14-22
Author(s):  
D. V. Gribanov
Keyword(s):  
Distributed Computing ◽  
Data Storage ◽  
Storage Systems ◽  
Legal Regulation ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Public Control ◽  
Blockchain Technology ◽  
Legal Method ◽  
Digital Assets

Introduction. This article is devoted to legal regulation of digital assets turnover, utilization possibilities of distributed computing and distributed data storage systems in activities of public authorities and entities of public control. The author notes that some national and foreign scientists who study a “blockchain” technology (distributed computing and distributed data storage systems) emphasize its usefulness in different activities. Data validation procedure of digital transactions, legal regulation of creation, issuance and turnover of digital assets need further attention.Materials and methods. The research is based on common scientific (analysis, analogy, comparing) and particular methods of cognition of legal phenomena and processes (a method of interpretation of legal rules, a technical legal method, a formal legal method and a formal logical one).Results of the study. The author conducted an analysis which resulted in finding some advantages of the use of the “blockchain” technology in the sphere of public control which are as follows: a particular validation system; data that once were entered in the system of distributed data storage cannot be erased or forged; absolute transparency of succession of actions while exercising governing powers; automatic repeat of recurring actions. The need of fivefold validation of exercising governing powers is substantiated. The author stresses that the fivefold validation shall ensure complex control over exercising of powers by the civil society, the entities of public control and the Russian Federation as a federal state holding sovereignty over its territory. The author has also conducted a brief analysis of judicial decisions concerning digital transactions.Discussion and conclusion. The use of the distributed data storage system makes it easier to exercise control due to the decrease of risks of forge, replacement or termination of data. The author suggests defining digital transaction not only as some actions with digital assets, but also as actions toward modification and addition of information about legal facts with a purpose of its establishment in the systems of distributed data storage. The author suggests using the systems of distributed data storage for independent validation of information about activities of the bodies of state authority. In the author’s opinion, application of the “blockchain” technology may result not only in the increase of efficiency of public control, but also in the creation of a new form of public control – automatic control. It is concluded there is no legislation basis for regulation of legal relations concerning distributed data storage today.

Towards a distributed data fusion pipeline for pedestrian behaviour analysis

2020 ◽  
Author(s):  
Sara Ferreira ◽  
Thiago RPM Rúbioa ◽  
João Jacob ◽  
Henrique Lopes Cardoso ◽  
Daniel Castro Silva ◽  
...  
Keyword(s):  
Data Fusion ◽  
Distributed Data ◽  
Behaviour Analysis ◽  
Distributed Data Fusion

Uji Komparasi Abnormal Return, Trading Volume, Trading Frequency, Dan Bid-Ask Spread Sebelum Dan Sesudah Share Split

2016 ◽  
Vol 8 (2) ◽  
pp. 24-45
Author(s):  
Tania Hayu Safira ◽  
Febryanti Simon
Keyword(s):  
Abnormal Return ◽  
Trading Volume ◽  
Stock Exchange ◽  
Distributed Data ◽  
Trading Frequency ◽  
Bid Ask Spread ◽  
Analysis Technique ◽  
Before And After ◽  
Test Of Hypothesis ◽  
Kolmogorov Smirnov

This study is event study that was conduct to examine the differences of abnormal return, trading volume, trading frequency and bid-ask spread before and after the events of share split. The object of this research is the companies that did share split and listed in Indonesia Stock Exchange in 2008 - 2015. The samples are 30 companies chosen by purposive sampling method. The criteria are the company did not do corporate action right issue, pre-emptive rights, a share dividend and bonus shares in the same year with share split. Event window used in this study was 30 days consisting of 15 days before and 15 days after the share split. Data analysis technique begins with a test of normality using Kolmogorov – Smirnov and transform for unnormally distributed data. Then, test of hypothesis using Paired t – test to compare the differences before and after share split. The results of this study showed that volume trading activity and trading frequency had significant differences before and after the share split. While, variable abnormal return and bid-ask spread had not significant differences before and after the share split. Keywords: Abnormal return, bid-ask spread, share split, trading frequency, trading volume.

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