Comparative Analysis Of Cryptocurrencies

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Comparative Analysis Of Cryptocurrencies

Abstract:

In the last decade, numbers of papers were published about blockchain and cryptocurrencies. We evaluated a portion of the well-known cryptocurrencies, and every cryptocurrency has its own pros and cons. It is a tedious task for the researchers to select a cryptocurrency, which is ideal to implement and also demonstrates better outcome on a diverse dataset with respect to performance, energy, time and security. These are the issues, which are confronted by the researchers. So our work concentrates on the usage of the 3 best-known cryptocurrencies , which are Bitcoin, Ripple, and Ethereum. We are going to evaluate one by one, and as per our results bitcoin is the best system among the other two strategies with respect to performance, ethereum seems to be more power efficient, and ripple is more secure than both of them.

Keywords–Blockchain, Cryptocurrencies, Bitcoin, Digital Currency.

Introduction

The blockchain is an undeniably ingenious invention – the brainchild of a person or group of people known by the pseudonym, Satoshi Nakamoto. But since then, it has evolved into something greater, and the main question every single person is asking is: What is Blockchain?

By allowing digital information to be distributed but not copied, blockchain technology created the backbone of a new type of internet. Originally devised for the digital currency, Bitcoin, the tech community has now found other potential uses for the technology.

According to Zibin et al [1], blockchain and bitcoin has enjoyed a huge success with the capital market reaching 10 billion dollars in 2016. Additionally, blockchain technology is becoming one of the most promising technologies for the next generation of Internet interaction systems, such as smart contracts , public services , Internet of Things (IoT) , reputation systems and security services .

In another paper, Stephen et al [2], Bitcoin has spiked once again in recent months, for example, the UK government is considering paying out research grants in Bitcoin; an increasing number of IT companies are stockpiling Bitcoin to defend against ransomware; growing numbers in China are buying into Bitcoin and seeing it as an investment opportunity.

Although blockchain was introduced in 2008, till then it became such a rapidly growing invention that almost every industry is shifting their data management system to it. Block chain has attracted 40 major banks and financial institutions [3]. IBM has 1,000 employees working on blockchain-powered projects. They’ve also set aside $200 million for development. Financial and tech firms invested an estimate $1.4 billion dollars in blockchain in 2016 with an increase to $2.1 billion dollars in 2018 [4].

Figure 1 gives an overview of the whole process of blockchain. We have done a comparative analysis of different type of cryptocurrencies and extracted different type of parameters. Following is a pictorial representation of how two parties can connect using blockchain….

Figure 1: Blockchain’s life cycle[image: ]

Figure 2: Block Structure[image: ]

This paper is divided in 3 sections. Section 1 presents comparative study. Section 2 presents conclusion and last section presents the reference.

Related Work

Blockchain and cryptocurrencies are becoming a revolutionary industry of 21st century, but it has it’s own pros and cons. main issue is the power consumption as the mining process involved in blockchain takes a huge amount of energy and cost. According to christophe et al, bitcoin is consuming 47 THh per year [4], and it’s a huge amount of energy consumption main reason for such amount of energy consumption is proof of work. PoW secures a blockchain network by creating random math problem which miners are in a race to solve. The winner is validated by other miners who confirm that the winner had correctly solved the math problem and downloaded the information on the previous block. With bitcoin, PoW is what adds new blocks to the chain, and is what rewards miners for adding and validating the correct blocks with bitcoin. In the beginning of bitcoin, people mining it sold it to speculators for the amount that it increased their electricity bills. Over time, as people found uses for the currency, and realized that because there could ever only be 21 million Bitcoin in existence, the price went up. As the price rose, more miners were incentivized to discover bitcoins and enter the market and to find ways to get an edge through more computing power and economies of scale.

Our work relies on the comparison of the most widely used digital currencies, in order to check which of the digital currency is most suitable for the end users.

In this section we gave a brief overview of the digital currencies. Section 2.1 defines bitcoin. Section 2.2 defines Etherem, and section 2.3 presents Ripple.

2.1. Bitcoin

A cryptocurrency, a form of electronic cash. It is a decentralized digital currency without a central bank or single administrator that can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries.

Following are some of the basic properties of bitcoin.

32 bit block size.
Uses 256 bit SHA-256 hash code.
32 bit nonce.
32 bit block version number.
32 bit time stamp.
Uses proof of work.
Limit of block size is 1 MB.
Currency name bitcoin.

2.2. Ethereum

Ethereum is a global, open-source platform for decentralized applications. On Ethereum, you can write code that controls digital value, runs exactly as programmed, and is accessible anywhere in the world.

Following are some basic points of ethereum.

32 bit block size.
Uses 256 bit SHA-256 hash code.
Uses proof of work.
Limit of block size is 1 MB.
Currency name ether.
Contains Halting problem.

2.3. Ripple

Ripple is a currency exchange and remittance network created by Ripple Labs Inc., a US-based technology company. Released in 2012, Ripple is built upon a distributed open source protocol, and supports tokens representing fiat currency, cryptocurrency, commodities, or other units of value such as frequent flier miles or mobile minutes. Ripple purports to enable ‘secure, instantly and nearly free global financial transactions of any size with no chargebacks.”

Much faster than both the other currencies.
Currency name XRP.
Can handle 1500 transactions per second.
Uses proof of stake.
Uses 256 bit SHA-256 hash code.

Comparative Analysis

We have done a comparative analysis on the characterization of ads and extracted five main parameters and fifteen sub parameters, to show what parameters are involved in ads delivery. e analysed 30 papers and plot the extracted parameters in tables. Following are the tables with some description.

Table 1:Economics.

Economics

Bitcoin

Ethereum

Ripple

Ownership

Public

Private

Public

Transaction speed

App. 1 hour

12-14 sec

3-5 sec

Transaction cost

$40

$14.40

$0.004

Native Currency

BTC

ETH

XPR

Maximum supply

21 million BTC

100 billion XPR

Table 1 presents the characterization of ads with 5 different sub-parameters; Location-based ads, random ads, generic ads, targeted ads and contextual ads. Location-based ads pinpoint consumers location and provide location-specific advertisements on their mobile devices. Random ads or pop-ups are forms of online advertising on the World Wide Web. A pop-up is a Graphical User Interface (GUI) display area, usually a small window, that suddenly appears (‘pops up’) in the foreground of the visual interface. The pop-up window containing an advertisement is usually generated by JavaScript that uses the cross-site scripting (XSS), sometimes with a secondary payload that uses Adobe Flash. They can also be generated by other vulnerabilities/ security holes in browser security. Generic ad tends to use mass media as a way to promote the firm. TV, radio, billboard, magazine, newspaper and website ads are common forms of mass media. The ads highlight the name of the firm, and perhaps the firm’s contact information as well, and are designed to generate public awareness of the firm. They will not, however, mention specific recommendations that the firm is currently making or has made in the past. Targeted ads are a form of advertising where online advertisers can use sophisticated methods to target the most receptive audiences with certain traits, based on the product or person the advertiser is promoting. These traits can either be demographic which are focused on race, economic status, gender, age, the level of education, income level and employment or they can be psychographically focused which are based on the consumer’s values, personality, attitudes, opinions, lifestyles, and interests. They can also be behavioral variables, such as browser history, purchase history, and other recent activity. Contextual ads is a form of targeted advertising for advertisements appearing on websites or other media, such as content displayed in mobile browsers. The advertisements themselves are selected and served by automated systems based on the identity of the user and the content displayed.

In paper [1] the location and contextual based ads are discussed. In paper [2, 13] random ads, generic and targeted ads are discussed. In paper [4 – 6] and [25, 28], importance of contextual ads are discussed. Paper [10, 11] and [27, 30] location, targeted and contextual based ads are discussed.

Table 2: Security

Security

Bitcoin

Ethereum

Ripple

Double spending attack

Yes

Sybil

Yes

51%

Yes

Dos

Yes

Table 2 presents the contextual ads, attribute targeting and behavioral targeting. Contextual ads is a form of targeted advertising for advertisements appearing on websites or other media, such as content displayed in mobile browsers. The advertisements themselves are selected and served by automated systems based on the identity of the user and the content displayed. Attribute targeting focuses on the user personal information. In behavioural targeting when a consumer uses an application the amount of time they view each page, the links they click on, the searches they make, and the things that they interact with, allows to collect that data, and other factors, to create a ‘profile’ that links to that consumer’s app As a result, ad publishers can use this data to create defined audience segments based on consumer that has similar profiles. Contextual ads have 3 sub-parameters; Keyword-based, ads category and relevant info. Keyword based is tasked with the automatic identification of terms that best describe the subject of a document. Key phrases, key terms, key segments or just keywords are the terminology which is used for defining the terms that represent the most relevant information contained in the document.

In paper [1] keyword based and user profile base ads are discussed. In paper [2] ads category is discussed. In paper [4] user profile is discussed. In paper [5, 6] user profile and relevant information is discussed. In paper [7] relevant information is discussed.

Table 3: Consensus mechanism

Consensus mechanism

Bitcoin

Ethereum

Ripple

Verification method

Proof of work

Proof of stake

Hash algorithm

SHA-256

Keccak-256

SHA-512 half

Block time

App. 10

1500

Energy Cost

App. 250 kWh

50kWh

33kWh

Mining reward

Yes

Table 3 presents the ads domain. Ads domain contains nature of ads i.e; which type of ads are to be displayed. It contains 11 sub parameters. Games, art, entertainment, business, dining, food, energy, medical and news feeds.

In paper [1] games, medical, art and shopping ads are discussed. In paper [2] games, energy and food ads are discussed. Paper [4] presents the ads about games.

Conclusion

After comprehensive analysis we analysed that most of papers have characterised ads in to 4 major categories, some of them have explained each category one by one in detail and introduced subcategories as well, while few of them just went through these categories. Some papers considered same context for different purpose for example; In Paper [1] Imdad et al, says that contextual ads contains only those ads which are based on users history while in paper [4] nath et al, says that contextual ads may also contain user profile information. We analysed that there are many papers who does not cover security point of view while in our view, it is one of most important part, those papers lack with. Most of papers related to ad’s characterisation, only considered admob while in our view there are many other ad’s sdks which needs to be covered . We analysed very few papers on reverse engineered sdks libraries and demonstrated network flow while other just gave an overview which was not enough to understand proper functioning of that whole work demonstrated in respective paper. Android market is a large plate-form for app developers, to earn revenue and there are still research gaps in this domain. Such as developers create an app and monitize it with google ads but get ditched by advertisers (google) and when developers claim for the money, google sometime does not let them to claim . This part should be considered in future by researchers. Personal information is an important part of someone. No-one has covered which information is being leaked at what time, cause some sdks specially, Admob changes it’s behaviour dynamically with time as admob new version comes in to the market everyone who is synced with google get it’s version updated which is very critical for user who should be aware what information is being shared to other parties by google.

References

GameHouse. Mobile advertising statistics—5 big trends you need to know! http://partners.gamehouse.com/ mobile- advertising- statistics- 5- big- trends- need- know/.
Ullah, Imdad, Roksana Boreli, Mohamed Ali Kaafar, and Salil S. Kanhere. ‘Characterising user targeting for in-app mobile ads.’ In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pp. 547-552. IEEE, 2014.
Nath, Suman. ‘Madscope: Characterizing mobile in-app targeted ads.’ In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pp. 59-73. ACM, 2015.
Rula, John P., Byungjin Jun, and Fabian Bustamante. ‘Mobile ad (d): Estimating mobile app session times for better ads.’ In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, pp. 123-128. ACM, 2015.
Nath, Suman, Felix Xiaozhu Lin, Lenin Ravindranath, and Jitendra Padhye. ‘SmartAds: bringing contextual ads to mobile apps.’ In Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pp. 111-124. ACM, 2013.
Hardt, Michaela, and Suman Nath. ‘Privacy-aware personalization for mobile advertising.’ In Proceedings of the 2012 ACM conference on Computer and communications security, pp. 662-673. ACM, 2012.
Khan, Azeem J., Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Balan, and Srinivasan Seshan. ‘CAMEO: A middleware for mobile advertisement delivery.’ In Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pp. 125-138. ACM, 2013.
Chen, Xiaomeng, Abhilash Jindal, and Y. Charlie Hu. ‘How much energy can we save from prefetching ads?: energy drain analysis of top 100 apps.’ In Proceedings of the Workshop on Power-Aware Computing and Systems, p. 3. ACM, 2013.
Vallina-Rodriguez, Narseo, Jay Shah, Alessandro Finamore, Yan Grunenberger, Konstantina Papagiannaki, Hamed Haddadi, and Jon Crowcroft. ‘Breaking for commercials: characterizing mobile advertising.’ In Proceedings of the 2012 ACM conference on Internet measurement conference, pp. 343-356. ACM, 2012.
Tongaonkar, Alok, Shuaifu Dai, Antonio Nucci, and Dawn Song. ‘Understanding mobile app usage patterns using in-app advertisements.’ In International Conference on Passive and Active Network Measurement, pp. 63-72. Springer, Berlin, Heidelberg, 2013.
Vallina-Rodriguez, Narseo, Srikanth Sundaresan, Abbas Razaghpanah, Rishab Nithyanand, Mark Allman, Christian Kreibich, and Phillipa Gill. ‘Tracking the trackers: Towards understanding the mobile advertising and tracking ecosystem.’ arXiv preprint arXiv:1609.07190 (2016).
Son, Sooel, Daehyeok Kim, and Vitaly Shmatikov. ‘What Mobile Ads Know About Mobile Users.’ In NDSS. 2016.
Grace, Michael C., Wu Zhou, Xuxian Jiang, and Ahmad-Reza Sadeghi. ‘Unsafe exposure analysis of mobile in-app advertisements.’ In Proceedings of the fifth ACM conference on Security and Privacy in Wireless and Mobile Networks, pp. 101-112. ACM, 2012.
Hardt, Michaela, and Suman Nath. ‘Privacy-aware personalization for mobile advertising.’ In Proceedings of the 2012 ACM conference on Computer and communications security, pp. 662-673. ACM, 2012.Hardt, Michaela, and Suman Nath. ‘Privacy-aware personalization for mobile advertising.’ In Proceedings of the 2012 ACM conference on Computer and communications security, pp. 662-673. ACM, 2012.
iAd. http://advertising.apple.com/.
Iqbal, Shamsi T., and Brian P. Bailey. ‘Effects of intelligent notification management on users and their tasks.’ In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 93-102. ACM, 2008.
Khan, Azeem J., Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Balan, and Srinivasan Seshan. ‘CAMEO: A middleware for mobile advertisement delivery.’ In Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pp. 125-138. ACM, 2013.
Kim, Byoungjip, Jin-Young Ha, SangJeong Lee, Seungwoo Kang, Youngki Lee, Yunseok Rhee, Lama Nachman, and Junehwa Song. ‘Adnext: a visit-pattern-aware mobile advertising system for urban commercial complexes.’ In Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, pp. 7-12. ACM, 2011.
Li, Lihong, Wei Chu, John Langford, and Robert E. Schapire. ‘A contextual-bandit approach to personalized news article recommendation.’ In Proceedings of the 19th international conference on World wide web, pp. 661-670. ACM, 2010.
Microsoft advertising. http://advertising.microsoft.com/.
Mittal, Radhika, Aman Kansal, and Ranveer Chandra. ‘Empowering developers to estimate app energy consumption.’ In Proceedings of the 18th annual international conference on Mobile computing and networking, pp. 317-328. ACM, 2012
Mohan, Prashanth, Suman Nath, and Oriana Riva. ‘Prefetching mobile ads: Can advertising systems afford it?.’ In Proceedings of the 8th ACM European Conference on Computer Systems, pp. 267-280. ACM, 2013.
Microsoft mobile ad control. http://advertising.microsoft.com/mobile-apps.
Ovide, Shira, and Greg Bensinger. ‘Mobile ads: Here’s what works and what doesn’t.’ The Wall Street Journal (2012)
Pathak, Abhinav, Y. Charlie Hu, and Ming Zhang. ‘Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof.’ In Proceedings of the 7th ACM european conference on Computer Systems, pp. 29-42. ACM, 2012.
Perzold, Charles. ‘Microsoft Silverlight Edition: Programming Windows Phone 7.’ (2010).
Ravindranath, Lenin, Jitendra Padhye, Sharad Agarwal, Ratul Mahajan, Ian Obermiller, and Shahin Shayandeh. ‘AppInsight: Mobile App Performance Monitoring in the Wild.’ In OSDI, vol. 12, pp. 107-120. 2012.
Ribeiro-Neto, Berthier, Marco Cristo, Paulo B. Golgher, and Edleno Silva de Moura. ‘Impedance coupling in content-targeted advertising.’ In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 496-503. ACM, 2005.
Wu, Xiaoyuan, and Alvaro Bolivar. ‘Keyword extraction for contextual advertisement.’ In Proceedings of the 17th international conference on World Wide Web, pp. 1195-1196. ACM, 2008.
Yahoo! Smart Ads. http://advertising.yaoo.com/marketing/smartads/.
Yahoo publisher network. http://advertisingcentral.yahoo.com/publisher/index.
Yan, Jun, Ning Liu, Gang Wang, Wen Zhang, Yun Jiang, and Zheng Chen. ‘How much can behavioral targeting help online advertising?.’ In Proceedings of the 18th international conference on World wide web, pp. 261-270. ACM, 2009.
Yih, Wen-tau, Joshua Goodman, and Vitor R. Carvalho. ‘Finding advertising keywords on web pages.’ In Proceedings of the 15th international conference on World Wide Web, pp. 213-222. ACM, 2006.

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