132,186 research outputs found
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
Gay and bisexual men’s perceptions of the donation and use of human biological samples for research: a qualitative study
Human biological samples (biosamples) are increasingly important in diagnosing, treating and measuring the prevalence of illnesses. For the gay and bisexual population, biosample research is particularly important for measuring the prevalence of human immunodeficiency virus (HIV). By determining people’s understandings of, and attitudes towards, the donation and use of biosamples, researchers can design studies to maximise acceptability and participation. In this study we examine gay and bisexual men’s attitudes towards donating biosamples for HIV research. Semi-structured telephone interviews were conducted with 46 gay and bisexual men aged between 18 and 63 recruited in commercial gay scene venues in two Scottish cities. Interview transcripts were analysed thematically using the framework approach. Most men interviewed seemed to have given little prior consideration to the issues. Participants were largely supportive of donating tissue for medical research purposes, and often favourable towards samples being stored, reused and shared. Support was often conditional, with common concerns related to: informed consent; the protection of anonymity and confidentiality; the right to withdraw from research; and ownership of samples. Many participants were in favour of the storage and reuse of samples, but expressed concerns related to data security and potential misuse of samples, particularly by commercial organisations. The sensitivity of tissue collection varied between tissue types and collection contexts. Blood, urine, semen and bowel tissue were commonly identified as sensitive, and donating saliva and as unlikely to cause discomfort. To our knowledge, this is the first in-depth study of gay and bisexual men’s attitudes towards donating biosamples for HIV research. While most men in this study were supportive of donating tissue for research, some clear areas of concern were identified. We suggest that these minority concerns should be accounted for to develop inclusive, evidence-informed research protocols that balance collective benefits with individual concerns
Plausibilistic Entropy and Anonymity *
Abstract A common approach behind measuring anonymity is that the larger the anonymity set is the higher the degree of anonymity it supports. Our approach builds upon this intuition proposing a very general and yet precise measure for security properties. Introduced in a paper accepted for ARES 2013 conference, plausibilistic entropy promises to offer an expressive and cost effective solution for quantifying anonymity. This article focuses on a detailed side-by-side comparison between plausibilistic entropy and Shannon entropy and underlines a promising level of compatibility between the two of them. Towards the end we present our vision on how to define a measure for anonymity based on plausibilistic entropy and how such a definition can be employed to serve practical purposes
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
RAPTOR: Routing Attacks on Privacy in Tor
The Tor network is a widely used system for anonymous communication. However,
Tor is known to be vulnerable to attackers who can observe traffic at both ends
of the communication path. In this paper, we show that prior attacks are just
the tip of the iceberg. We present a suite of new attacks, called Raptor, that
can be launched by Autonomous Systems (ASes) to compromise user anonymity.
First, AS-level adversaries can exploit the asymmetric nature of Internet
routing to increase the chance of observing at least one direction of user
traffic at both ends of the communication. Second, AS-level adversaries can
exploit natural churn in Internet routing to lie on the BGP paths for more
users over time. Third, strategic adversaries can manipulate Internet routing
via BGP hijacks (to discover the users using specific Tor guard nodes) and
interceptions (to perform traffic analysis). We demonstrate the feasibility of
Raptor attacks by analyzing historical BGP data and Traceroute data as well as
performing real-world attacks on the live Tor network, while ensuring that we
do not harm real users. In addition, we outline the design of two monitoring
frameworks to counter these attacks: BGP monitoring to detect control-plane
attacks, and Traceroute monitoring to detect data-plane anomalies. Overall, our
work motivates the design of anonymity systems that are aware of the dynamics
of Internet routing
On the Measurement of Privacy as an Attacker's Estimation Error
A wide variety of privacy metrics have been proposed in the literature to
evaluate the level of protection offered by privacy enhancing-technologies.
Most of these metrics are specific to concrete systems and adversarial models,
and are difficult to generalize or translate to other contexts. Furthermore, a
better understanding of the relationships between the different privacy metrics
is needed to enable more grounded and systematic approach to measuring privacy,
as well as to assist systems designers in selecting the most appropriate metric
for a given application.
In this work we propose a theoretical framework for privacy-preserving
systems, endowed with a general definition of privacy in terms of the
estimation error incurred by an attacker who aims to disclose the private
information that the system is designed to conceal. We show that our framework
permits interpreting and comparing a number of well-known metrics under a
common perspective. The arguments behind these interpretations are based on
fundamental results related to the theories of information, probability and
Bayes decision.Comment: This paper has 18 pages and 17 figure
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
- …