arXiv subjects

Haaroon Yousaf

Publications and source records attributed to Haaroon Yousaf.

10 recordsLinked to original sources

arXiv2023

Raphtory: The temporal graph engine for Rust and Python

Raphtory is a platform for building and analysing temporal networks. The library includes methods for creating networks from a variety of data sources; algorithms to explore their structure and evolution; and an extensible GraphQL server for deployment of applications built on top. Raphtory's core engine is built in Rust, for efficiency, with Python interfaces, for ease of use. Raphtory is developed by network scientists, with a background in Physics, Applied Mathematics, Engineering and Computer Science, for use across academia and industry.

cs.SI↗

arXiv2023

Non-Markovian paths and cycles in NFT trades

Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.

cs.SI↗

arXiv2022

How to Peel a Million: Validating and Expanding Bitcoin Clusters

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long line of research has demonstrated that Bitcoin is not anonymous. This has been perhaps best exemplified by the development of clustering heuristics, which have in turn given rise to the ability to track the flow of bitcoins as they are sent from one entity to another. In this paper, we design a new heuristic that is designed to track a certain type of flow, called a peel chain, that represents many transactions performed by the same entity; in doing this, we implicitly cluster these transactions and their associated pseudonyms together. We then use this heuristic to both validate and expand the results of existing clustering heuristics. We also develop a machine learning-based validation method and, using a ground-truth dataset, evaluate all our approaches and compare them with the state of the art. Ultimately, our goal is to not only enable more powerful tracking techniques but also call attention to the limits of anonymity in these systems.

cs.CR↗

arXiv2022

Investigating transactions in cryptocurrencies

This thesis presents techniques to investigate transactions in uncharted cryptocurrencies and services. Cryptocurrencies are used to securely send payments online. Payments via the first cryptocurrency, Bitcoin, use pseudonymous addresses that have limited privacy and anonymity guarantees. Research has shown that this pseudonymity can be broken, allowing users to be tracked using clustering and tagging heuristics. Such tracking allows crimes to be investigated. If a user has coins stolen, investigators can track addresses to identify the destination of the coins. This, combined with an explosion in the popularity of blockchain, has led to a vast increase in new coins and services. These offer new features ranging from coins focused on increased anonymity to scams shrouded as smart contracts. In this study, we investigated the extent to which transaction privacy has improved and whether users can still be tracked in these new ecosystems. We began by analysing the privacy-focused coin Zcash, a Bitcoin-forked cryptocurrency, that is considered to have strong anonymity properties due to its background in cryptographic research. We revealed that the user anonymity set can be considerably reduced using heuristics based on usage patterns. Next, we analysed cross-chain transactions collected from the exchange ShapeShift, revealing that users can be tracked as they move across different ledgers. Finally, we present a measurement study on the smart-contract pyramid scheme Forsage, a scam that cycled $267 million USD (of Ethereum) within its first year, showing that at least 88% of the participants in the scheme suffered a loss. The significance of this study is the revelation that users can be tracked in newer cryptocurrencies and services by using our new heuristics, which informs those conducting investigations and developing these technologies.

cs.CR↗

arXiv2021

Forsage: Anatomy of a Smart-Contract Pyramid Scheme

Pyramid schemes are investment scams in which top-level participants in a hierarchical network recruit and profit from an expanding base of defrauded newer participants. Pyramid schemes have existed for over a century, but there have been no in-depth studies of their dynamics and communities because of the opacity of participants' transactions. In this paper, we present an empirical study of Forsage, a pyramid scheme implemented as a smart contract and at its peak one of the largest consumers of resources in Ethereum. As a smart contract, Forsage makes its (byte)code and all of its transactions visible on the blockchain. We take advantage of this unprecedented transparency to gain insight into the mechanics, impact on participants, and evolution of Forsage. We quantify the (multi-million-dollar) gains of top-level participants as well as the losses of the vast majority (around 88%) of users. We analyze Forsage code both manually and using a purpose-built transaction simulator to uncover the complex mechanics of the scheme. Through complementary study of promotional videos and social media, we show how Forsage promoters have leveraged the unique features of smart contracts to lure users with false claims of trustworthiness and profitability, and how Forsage activity is concentrated within a small number of national communities.

cs.CR↗

arXiv2021

An Empirical Analysis of Privacy in the Lightning Network

Payment channel networks, and the Lightning Network in particular, seem to offer a solution to the lack of scalability and privacy offered by Bitcoin and other blockchain-based cryptocurrencies. Previous research has focused on the scalability, availability, and crypto-economics of the Lightning Network, but relatively little attention has been paid to exploring the level of privacy it achieves in practice. This paper presents a thorough analysis of the privacy offered by the Lightning Network, by presenting several attacks that exploit publicly available information about the network in order to learn information that is designed to be kept secret, such as how many coins a node has available or who the sender and recipient are in a payment routed through the network.

cs.CR↗

arXiv2019

Tracing Transactions Across Cryptocurrency Ledgers

One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have evertaken place, is globally visible. As one consequenceof this degree of transparency, a long line of recent re-search has demonstrated that even in cryptocurrenciesthat are specifically designed to improve anonymity it is often possible to track money as it changes hands,and in some cases to de-anonymize users entirely. With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether ornot money can be traced as it moves within the ledgerof a single cryptocurrency, but if it can in fact be tracedas it moves across ledgers. This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades. In this paper, weuse data scraped from ShapeShift over a thirteen-monthperiod and the data from eight different blockchains to explore this question. Beyond developing new heuristics and creating new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whetherthey serve a criminal or a profit-driven agenda.

cs.CR↗

arXiv2019

Incentivising Privacy in Cryptocurrencies

Privacy was one of the key points mentioned in Nakamoto's Bitcoin whitepaper, and one of the selling points of Bitcoin in its early stages. In hindsight, however, de-anonymising Bitcoin users turned out to be more feasible than expected. Since then, privacy focused cryptocurrencies such as Zcash and Monero have surfaced. Both of these examples cannot be described as fully successful in their aims, as recent research has shown. Incentives are integral to the security of cryptocurrencies, so it is interesting to investigate whether they could also be aligned with privacy goals. A lack of privacy often results from low user counts, resulting in low anonymity sets. Could users be incentivised to use the privacy preserving implementations of the systems they use? Not only is Zcash much less used than Bitcoin (which it forked from), but most Zcash transactions are simply transparent transactions, rather than the (at least intended to be) privacy-preserving shielded transactions. This paper and poster briefly discusses how incentives could be incorporated into systems like cryptocurrencies with the aim of achieving privacy goals. We take Zcash as example, but the ideas discussed could apply to other privacy-focused cryptocurrencies. This work was presented as a poster at OPERANDI 2018, the poster can be found within this short document.

cs.CR↗

arXiv2018

Why is a Ravencoin Like a TokenDesk? An Exploration of Code Diversity in the Cryptocurrency Landscape

Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities.

cs.CR↗

arXiv2018

An Empirical Analysis of Anonymity in Zcash

Among the now numerous alternative cryptocurrencies derived from Bitcoin, Zcash is often touted as the one with the strongest anonymity guarantees, due to its basis in well-regarded cryptographic research. In this paper, we examine the extent to which anonymity is achieved in the deployed version of Zcash. We investigate all facets of anonymity in Zcash's transactions, ranging from its transparent transactions to the interactions with and within its main privacy feature, a shielded pool that acts as the anonymity set for users wishing to spend coins privately. We conclude that while it is possible to use Zcash in a private way, it is also possible to shrink its anonymity set considerably by developing simple heuristics based on identifiable patterns of usage.

cs.CR↗