Towards Measuring The Fungibility and Anonymity of Cryptocurrencies

Cryptocurrencies aim to replicate physical cash in the digital realm while removing centralized middlemen. Decentralization is achieved by the blockchain, a permanent public ledger that contains a record of every transaction. The public ledger ensures transparency, which enables public verifiability but harms fungibility and anonymity. Even though cryptocurrencies attracted millions of users in the last decade with their total market cap reaching approximately one trillion USD, their anonymity guarantees are poorly understood. Indeed, previous notions of privacy, anonymity, and fungibility for cryptocurrencies are either non-quantitative or inapplicable, e.g., computationally hard to measure. In this work, we put forward a formal framework to measure the fungibility and anonymity of cryptocurrencies, allowing us to quantitatively reason about the mixing characteristics of cryptocurrencies and the privacy-enhancing technologies built on top of them. Our methods apply absorbing Markov chains combined with Shannon entropy. To the best of our knowledge, our work is the first to assess the fungibility of cryptocurrencies. Among other results, we find that in the studied one-week interval, the Bitcoin network, on average, provided comparable but quantifiably more fungibility than the Ethereum network.Comment: Pre-print. 23 page

Towards a Privacy Diagnosis Centre : Measuring k-anonymity

Most of the recent efforts addressing the issue of privacy have focused on devising algorithms for the anonymization and diversification of data

Quantifying and measuring anonymity

The design of anonymous communication systems is a relatively new field, but the desire to quantify the security these systems offer has been an important topic of research since its beginning. In recent years, anonymous communication systems have evolved from obscure tools used by specialists to mass-market software used by millions of people. In many cases the users of these tools are depending on the anonymity offered to protect their liberty, or more. As such, it is of critical importance that not only can we quantify the anonymity these tools offer, but that the metrics used represent realistic expectations, can be communicated clearly, and the implementations actually offer the anonymity they promise. This paper will discuss how metrics, and the techniques used to measure them, have been developed for anonymous communication tools including low-latency networks and high-latency email systems. © 2014 Springer-Verlag Berlin Heidelberg

You never surf alone. Ubiquitous tracking of users' browsing habits

In the early age of the internet users enjoyed a large level of anonymity. At the time web pages were just hypertext documents; almost no personalisation of the user experience was o ered. The Web today has evolved as a world wide distributed system following specific architectural paradigms. On the web now, an enormous quantity of user generated data is shared and consumed by a network of applications and services, reasoning upon users expressed preferences and their social and physical connections. Advertising networks follow users' browsing habits while they surf the web, continuously collecting their traces and surfing patterns. We analyse how users tracking happens on the web by measuring their online footprint and estimating how quickly advertising networks are able to pro le users by their browsing habits

An Empirical Analysis of Privacy in Cryptocurrencies

Cryptocurrencies have emerged as an important technology over the past decade and have, undoubtedly, become blockchain’s most popular application. Bitcoin has been by far the most popular out of the thousands of cryptocurrencies that have been created. Some of the features that made Bitcoin such a fascinating technology include its transactions being made publicly available and permanently stored, and the ability for anyone to have access. Despite this transparency, it was initially believed that Bitcoin provides anonymity to its users, since it allowed them to transact using a pseudonym instead of their real identity. However, a long line of research has shown that this initial belief was false and that, given the appropriate tools, Bitcoin transactions can indeed be traced back to the real-life entities performing them. In this thesis, we perform a survey to examine the anonymity aspect of cryptocurrencies. We start with early works that made first efforts on analysing how private this new technology was. We analyse both from the perspective of a passive observer with eyes only to the public immutable state of transactions, the blockchain, as well as from an observer who has access to network layer information. We then look into the projects that aimed to enhance the anonymity provided in cryptocurrencies and also analyse the evidence of how much they succeeded in practice. In the first part of our own contributions we present our own take on Bitcoin’s anonymity, inspired by the research already in place. We manage to extend existing heuristics and provide a novel methodology on measuring the confidence we have in our anonymity metrics, instead of looking into the issue from a binary perspective, as in previous research. In the second part we provide the first full-scale empirical work on measuring anonymity in a cryptocurrency that was built with privacy guarantees, based on a very well established cryptography, Zcash. We show that just building a tool which provides anonymity in theory is very different than the privacy offered in practice once users start to transact with it. Finally, we look into a technology that is not a cryptocurrency itself but is built on top of Bitcoin, thus providing a so-called layer 2 solution, the Lightning network. Again, our measurements showed some serious privacy concerns of this technology, some of which were novel and highly applicable

On the anonymity risk of time-varying user profiles.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version

The Measurement of Intellectual Influence

We examine the problem of measuring influence based on the information contained in the data on the communications between scholarly publications, judicial decisions, patents, web pages, and other entities. The measurement of influence is useful to address several empirical questions such as reputation, prestige, aspects of the diffusion of knowledge, the markets for scientists and scientific publications, the dynamics of innovation, ranking algorithms of search engines in the World Wide Web, and others. In this paper we ask why any given methodology is reasonable and informative applying the axiomatic method. We find that a unique ranking method can be characterized by means of five axioms: anonymity, invariance to citation intensity, weak homogeneity, weak consistency, and invariance to splitting of journals. This method is easily implementable and turns out to be different from those regularly used in social and natural sciences, arts and humanities, and computer science.Intellectual Influence, Citations, Ranking Methods, Consistency.

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system