Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSummarizing and Analyzing the Privacy-Preserving Techniques in Bitcoin and other Cryptocurrencies
221
0
0.0
(
0
)
Added by
Chaitanya Rahalkar
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Bitcoin and many other similar Cryptocurrencies have been in existence for over a decade, prominently focusing on decentralized, pseudo-anonymous ledger-based transactions. Many protocol improvements and changes have resulted in new variants of Cryptocurrencies that are known for their peculiar characteristics. For instance, Storjcoin is a Proof-of-Storage-based Cryptocurrency that incentivizes its peers based on the amount of storage owned by them. Cryptocurrencies like Monero strive for user privacy by using privacy-centric cryptographic algorithms. While Cryptocurrencies strive to maintain peer transparency by making the transactions and the entire ledger public, user privacy is compromised at times. Monero and many other privacy-centric Cryptocurrencies have significantly improved from the original Bitcoin protocol after several problems were found in the protocol. Most of these deficiencies were related to the privacy of users. Even though Bitcoin claims to have pseudo-anonymous user identities, many attacks have managed to successfully de-anonymize users. In this paper, we present some well-known attacks and analysis techniques that have compromised the privacy of Bitcoin and many other similar Cryptocurrencies. We also analyze and study different privacy-preserving algorithms and the problems these algorithms manage to solve. Lastly, we touch upon the ethics, impact, legality, and acceptance of imposing these privacy algorithms.
rate research
Read More
In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.
Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coins intrinsic value. This paper suggests a normative dual-value theory to assess the fundamental value of Bitcoin. We draw on the experience from the art market, where similar replication problems are prevalent. The idea is to decompose the total value of a cryptocurrency into two parts: one is its art value and the other is its use value. The tradeoff between these two values is also analyzed, which enlightens our proposal of an image coin for Bitcoin so as to elevate its use value without sacrificing its art value. To show the general validity of the dual-value theory, we also apply it to evaluate the prospects of four major cryptocurrencies. We find this framework is helpful for both the investors and the exchanges to examine a new coins value when it first appears in the market.
With mobile phone penetration rates reaching 90%, Consumer Proprietary Network Information (CPNI) can offer extremely valuable information to different sectors, including policymakers. Indeed, as part of CPNI, Call Detail Records have been successfully used to provide real-time traffic information, to improve our understanding of the dynamics of peoples mobility and so to allow prevention and measures in fighting infectious diseases, and to offer population statistics. While there is no doubt of the usefulness of CPNI data, privacy concerns regarding sharing individuals data have prevented it from being used to its full potential. Traditional de-anonymization measures, such as pseudonymization and standard de-identification, have been shown to be insufficient to protect privacy. This has been specifically shown on mobile phone datasets. As an example, researchers have shown that with only four data points of approximate place and time information of a user, 95% of users could be re-identified in a dataset of 1.5 million mobile phone users. In this landscape paper, we will discuss the state-of-the-art anonymization techniques and their shortcomings.
Resource Public Key Infrastructure (RPKI) is vital to the security of inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs) to unilaterally takedown IP prefixes - indeed, such attacks have been launched by nation-state adversaries. The threat of IP prefix takedowns is one of the factors hindering RPKI adoption. In this work, we propose the first distributed RPKI system, based on threshold signatures, that requires the coordination of a number of RIRs to make changes to RPKI objects; hence, preventing unilateral prefix takedown. We perform extensive evaluations using our implementation demonstrating the practicality of our solution. Furthermore, we show that our system is scalable and remains efficient even when RPKI is widely deployed.
In recent years, the data mining techniques have met a serious challenge due to the increased concerning and worries of the privacy, that is, protecting the privacy of the critical and sensitive data. Different techniques and algorithms have been already presented for Privacy Preserving data mining, which could be classified in three common approaches: Data modification approach, Data sanitization approach and Secure Multi-party Computation approach. This paper presents a Data modification- based Framework for classification and evaluation of the privacy preserving data mining techniques. Based on our framework the techniques are divided into two major groups, namely perturbation approach and anonymization approach. Also in proposed framework, eight functional criteria will be used to analyze and analogically assessment of the techniques in these two major groups. The proposed framework provides a good basis for more accurate comparison of the given techniques to privacy preserving data mining. In addition, this framework allows recognizing the overlapping amount for different approaches and identifying modern approaches in this field.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
