THE TEMTUM CONSENSUS ALGORITHM – A LOW ENERGY REPLACEMENT TO PROOF OF WORK
Richard Dennis and Gareth Owenson University of Portsmouth, Portsmouth, United Kingdom
This paper presents a novel consensus algorithm deployed within the Temtum cryptocurrency network. An overview of the proof of work consensus algorithm is presented, and gaps in the research are outlined. The Temtum consensus algorithm's unique components, including the Node Participation Document (NPD) and the use of the NIST randomness beacon, are outlined and explained. Comparisons on the cost to attack the consensus algorithm and energy consumption between the Temtum consensus algorithm and Bitcoin’s proof of work is presented and evaluated. We conclude this paper summarising the findings of the research and presenting future work to be conducted.
Blockchain, Peer-to-Peer Networks,Cryptocurrencies, Consensus, Byzantine Fault Tolerance.
Full Paper
https://aircconline.com/csit/papers/vol10/csit101301.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
SMART INSURANCE CONTRACT AGAINST POLITICAL RISKS: DEFINITIONS AND GENERAL REFLECTIONS
Remy Zgraggen University Oldenburg, Switzerland
IThe present research article shall outline how blockchain technology could be combined with insurance solutions against political risks. Through the definitions and the characterization of the key concepts of traditional insurance law and blockchain technology using case examples of specific political risks, it will be shown, how the insurance coverage of political risks could be achieved through smart insurance contracts in the future.
Political Risk Insurance, Smart Insurance Contract, Blockchain, Insurance Principle, Insurance Law, Insured Event.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101302.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
GDPR COMPLIANCE FOR BLOCKCHAIN APPLICATIONS IN HEALTHCARE
Anton Hasselgren1 , Paul Kengfai Wan2 , Margareth Horn3 , Katina Kralevska4 , Danilo Gligoroski4 and Arild Faxvaag1 1,2,3,4 Norwegian University of Science and Technology, Norway
The transparent and decentralized characteristics associated with blockchain can be both appealing and problematic when applied to a healthcare use-case. As health data is highly sensitive, it is therefore, highly regulated to ensure the privacy of patients. At the same time, access to health data and interoperability are in high demand. Regulatory frameworks such as GDPR and HIPAA are, amongst other objectives, meant to contribute to mitigating the risk of privacy violations of health data. Blockchain features can likely improve interoperability and access control to health data, and at the same time, preserve or even increase, the privacy of patients. Blockchain applications should address compliance with the current regulatory framework to increase real-world feasibility. This exploratory work indicates that published proof-of-concepts in the healthcare domain comply with GDPR, to an extent. Blockchain developers need to make design choices to be compliant with GDPR since currently, none available blockchain platform can show compliance out of the box.
Blockchain, DTL, health data, GDPR, privacy regulations
For More Details :
https://aircconline.com/csit/papers/vol10/csit101303.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
UNIQUE SOFTWARE ENGINEERING TECHNIQUES: PANACEA FOR THREAT COMPLEXITIES IN SECURE MULTIPARTY COMPUTATION (MPC) WITH BIG DATA
Uchechukwu Emejeamara1 , Udochukwu Nwoduh2 and Andrew Madu2 1Connecticut Section, USA 2Federal Polytechnic Nekede, Nigeria.
Most large corporations with big data have adopted more privacy measures in handling their sensitive/private data and as a result, employing the use of analytic tools to run across multiple sources has become ineffective. Joint computation across multiple parties is allowed through the use of secure multi-party computations (MPC). The practicality of MPC is impaired when dealing with large datasets as more of its algorithms are poorly scaled with data sizes. Despite its limitations, MPC continues to attract increasing attention from industry players who have viewed it as a better approach to exploiting big data. Secure MPC is however, faced with complexities that most times overwhelm its handlers, so the need for special software engineering techniques for resolving these threat complexities. This research presents cryptographic data security measures, garbed circuits protocol, optimizing circuits, and protocol execution techniques as some of the special techniques for resolving threat complexities associated with MPC’s. Honest majority, asymmetric trust, covert security, and trading off leakage are some of the experimental outcomes of implementing these special techniques. This paper also reveals that an essential approach in developing suitable mitigation strategies is having knowledge of the adversary type.
Cryptographic Data Security, Garbed Circuits, Optimizing Circuits, Protocol Execution, Honest Majority, Asymmetric Trust, Covert Security, Trading Off Leakage.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101413.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
DATA DRIVEN SOFT SENSOR FOR CONDITION MONITORING OF SAMPLE HANDLING SYSTEM (SHS)
Abhilash Pani, Jinendra Gugaliya and Mekapati Srinivas Industrial Automation Technology Centre, ABB, Bangalore, India
Gas sample is conditioned using sample handling system (SHS) to remove particulate matter and moisture content before sending it through Continuous Emission Monitoring (CEM) devices. The performance of SHS plays a crucial role in reliable operation of CEMs and therefore, sensor-based condition monitoring systems (CMSs) have been developed for SHSs. As sensor failures impact performance of CMSs, a data driven soft-sensor approach is proposed to improve robustness of CMSs in presence of single sensor failure. The proposed approach uses data of available sensors to estimate true value of a faulty sensor which can be further utilized by CMSs. The proposed approach compares multiple methods and uses support vector regression for development of soft sensors. The paper also considers practical challenges in building those models. Further, the proposed approach is tested on industrial data and the results show that the soft sensor values are in close match with the actual ones.
Sample Handling System, Soft-Sensor, Variance Inflation Factor (VIF), Local Outlier Factor (LOF), Support Vector Regression.
For More Details :
: https://aircconline.com/csit/papers/vol10/csit101423.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
TOWARDS A RISK ASSESSMENT MODEL FOR BIG DATA IN CLOUD COMPUTING ENVIRONMENT
Saadia Drissi, Soukaina Elhasnaoui, Hajar Iguer, Siham Benhadou and Hicham Medromi (EAS-LRI) Systems Architecture Team, ENSEM, Hassan II University Pluridisciplinary Laboratory of Research & Innovation (LPRI), EMSI Casablanca, Morocco
Cloud computing gives a relevant and adaptable support for Big Data by the ease of use, access to resources, low cost use of resources, and the use of strong equipment to process big data. Cloud and big data center on developing the value of a business while reducing capital costs. Big data and cloud computing, both favor companies and by cause of their benefit, the use of big data growths extremely in the cloud. With this serious increase, there are several emerging risk security concerns. Big data has more vulnerabilities with the comparison to classical database, as this database are stored in servers owned by the cloud provider. The various usage of data make safety-related big data in the cloud intolerable with the traditional security measures. The security of big data in the cloud needs to be looked at and discussed. In this current paper, my colleagues and me will present and discuss the risk assessment of big-data applications in cloud computing environments and present some ideas for assessing these risks. .
Cloud computing, risk assessment, big data, security
For More Details :
https://aircconline.com/csit/papers/vol10/csit101503.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
Parallel Data Extraction Using Word Embeddings
Pintu Lohar and Andy Way ADAPT Centre, Dublin City University, Ireland
Building a robust MT system requires a sufficiently large parallel corpus to be available as training data. In this paper, we propose to automatically extract parallel sentences from comparable corpora without using any MT system or even any parallel corpus at all. Instead, we use crosslingual information retrieval (CLIR), average word embeddings, text similarity and a bilingual dictionary, thus saving a significant amount of time and effort as no MT system is involved in this process. We conduct experiments on two different kinds of data: (i) formal texts from news domain, and (ii) user-generated content (UGC) from hotel reviews. The automatically extracted sentence pairs are then added to the already available parallel training data and the extended translation models are built from the concatenated data sets. Finally, we compare the performance of our new extended models against the baseline models built from the available data. The experimental evaluation reveals that our proposed approach is capable of improving the translation outputs for both the formal texts and UGC.
Machine Translation, parallel data, user-generated content, word embeddings, text similarity, comparable corpora.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101521.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
BLIND SQL INJECTION ATTACKS OPTIMIZATION
Ruben Ventura Independent Security Researcher
This paper presents new and evolved methods to perform Blind SQL Injection attacks. These are much faster than the current publicly available tools and techniques due to optimization and redesign ideas that hack databases in more efficient methods, using cleverer injection payloads; this is the result of years of private research. Implementing these methods within carefully crafted code has resulted in the development of the fastest tools in the world to extract information from a database through Blind SQL Injection vulnerabilities. These tools are around 1600% faster than the currently most popular tools. The nature of such attack vectors will be explained in this paper, including all of their intrinsic details. .
Web Application Security, Blind SQL Injection, Attack Optimization, New Exploitation Methods.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101909.pdf
Volume Link :
http://airccse.org/csit/V10N19.html
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