As interest in the practical use of cryptocurrencies continues to grow, so does the focus on the (perceived) privacy and anonymity of users within this domain. Despite this attention, there is a notable absence of standardized definitions for these terms. This article aims to address this gap by exploring the various interpretations of privacy, anonymity, and related concepts in the context of cryptocurrencies. Drawing from a thorough review of existing literature, we propose practical definitions for both privacy and anonymity. Utilizing these definitions, we introduce an ontology designed to streamline future research, identify knowledge gaps, and facilitate clearer communication in the field.

In this paper we propose and implement a digital permissioned decentralized anonymous payment scheme that finds a balance between anonymity and auditability. This approach allows banks to ensure that their clients are not participating in illegal financial transactions, whilst clients stay in control over their sensitive, personal information. Existing anonymous payment schemes often provide good privacy, but only little or mostly no auditability. We provide both by extending the Zerocash zk-SNARK based approach and adding functionality that allows for customer due diligence ‘at the gate’. Clients can do fully anonymous transactions up to a certain amount per time unit and larger transactions are forced to include verifiably encrypted transactions details that can only be opened by a select group of ‘judges’.

String sanitizers are widely used functions for preventing injection attacks such as SQL injections and cross-site scripting (XSS). It is therefore crucial that the implementations of such string sanitizers are correct. We present a novel approach to reason about a sanitizer's correctness by automatically generating a model of the implementation and comparing it to a model of the expected behaviour. To automatically derive a model of the implementation of the sanitizer, this paper introduces a black-box learning algorithm that derives a Symbolic Finite Transducer (SFT). This black-box algorithm uses membership and equivalence oracles to derive such a model. In contrast to earlier research, SFTs not only describe the input or output language of a sanitizer but also how a sanitizer transforms the input into the output. As a result, we can reason about the transformations from input into output that are performed by the sanitizer. We have implemented this algorithm in an open-source tool of which we show that it can reason about the correctness of non-trivial sanitizers within a couple of minutes without any adjustments to the existing sanitizers. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

We present DECANTeR, a system to detect anomalous outbound HTTP communication, which passively extracts fingerprints for each application running on a monitored host. The goal of our system is to detect unknown malware and backdoor communication indicated by unknown fingerprints extracted from a host's network traffic. We evaluate a prototype with realistic data from an international organization and datasets composed of malicious traffic. We show that our system achieves a false positive rate of 0.9% for 441 monitored host machines, an average detection rate of 97.7%, and that it cannot be evaded by malware using simple evasion techniques such as using known browser user agent values. We compare our solution with DUMONT [24], the current state-of-the-art IDS which detects HTTP covert communication channels by focusing on benign HTTP traffic. The results show that DECANTeR outperforms DUMONT in terms of detection rate, false positive rate, and even evasion-resistance. Finally, DECANTeR detects 96.8% of information stealers in our dataset, which shows its potential to detect data exfiltration.

Aggregation of time-series data offers the possibility to learn certain statistics over data periodically uploaded by different sources. In case of privacy sensitive data, it is desired to hide every data provider's individual values from the other participants (including the data aggregator). Existing privacy preserving time-series data aggregation schemes focus on the sum as aggregation means, since it is the most essential statistics used in many applications such as smart metering, participatory sensing, or appointment scheduling. However, all existing schemes have an important drawback: they do not provide verifiable outputs, thus users have to trust the data aggregator that it does not output fake values. We propose a publicly verifiable data aggregation scheme for privacy preserving time-series data summation. We prove its security and verifiability under the XDH assumption and a widely used, strong variant of the Co-CDH assumption. Moreover, our scheme offers low computation complexity on the users' side, which is essential in many applications.

We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of the search range. Next, locally similar tract segments are moved toward each other in an iterative process, resulting in a local contraction of tracts perpendicular to the local tract direction at a given scale. This not only leads to the abstraction of the global structure of the white matter as represented by the tracts, but also creates volumetric voids. This increase of empty space decreases the mutual occlusion of tracts and, consequently, results in a better understanding of the brain's three-dimensional fiber tract structure. Our implementation supports an interactive and continuous transition between the original and the abstracted representations via various scale levels of similarity. We also support the selection of groups of tracts, which are highlighted and rendered with the abstracted visualization as context.

Abstract We present an interactive illustrative visualization method inspired by traditional pen‐and‐ink illustration styles. Specifically, we explore how to provide context around DTI fiber tracts in the form of surfaces of the brain, the skull, or other objects such as tumors. These contextual surfaces are derived from either segmentation data or generated using interactive iso‐surface extraction and are rendered with a flexible, slice‐based hatching technique, controlled with ambient occlusion. This technique allows us to produce a consistent and frame‐coherent appearance with precise control over the lines. In addition, we provide context through cutting planes onto which we render gray matter with stippling. Together, our methods not only facilitate the interactive exploration and illustration of brain fibers within their anatomical context but also allow us to produce high‐quality images for print reproduction. We provide evidence for the success of our approach with an informal evaluation with domain experts.

We present the design and evaluation of FI3D, a direct-touch data exploration technique for 3D visualization spaces. The exploration of three-dimensional data is core to many tasks and domains involving scientific visualizations. Thus, effective data navigation techniques are essential to enable comprehension, understanding, and analysis of the information space. While evidence exists that touch can provide higher-bandwidth input, somesthetic information that is valuable when interacting with virtual worlds, and awareness when working in collaboration, scientific data exploration in 3D poses unique challenges to the development of effective data manipulations. We present a technique that provides touch interaction with 3D scientific data spaces in 7 DOF. This interaction does not require the presence of dedicated objects to constrain the mapping, a design decision important for many scientific datasets such as particle simulations in astronomy or physics. We report on an evaluation that compares the technique to conventional mouse-based interaction. Our results show that touch interaction is competitive in interaction speed for translation and integrated interaction, is easy to learn and use, and is preferred for exploration and wayfinding tasks. To further explore the applicability of our basic technique for other types of scientific visualizations we present a second case study, adjusting the interaction to the illustrative visualization of fiber tracts of the brain and the manipulation of cutting planes in this context.

We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.

Abstract In this paper we present several techniques to interactively explore representations of 2D vector fields. Through a set of simple hand postures used on large, touch‐sensitive displays, our approach allows individuals to custom‐design glyphs (arrows, lines, etc.) that best reveal patterns of the underlying dataset. Interactive exploration of vector fields is facilitated through freedom of glyph placement, glyph density control, and animation. The custom glyphs can be applied individually to probe specific areas of the data but can also be applied in groups to explore larger regions of a vector field. Re‐positionable sources from which glyphs—animated according to the local vector field—continue to emerge are used to examine the vector field dynamically. The combination of these techniques results in an engaging visualization with which the user can rapidly explore and analyze varying types of 2D vector fields, using a virtually infinite number of custom‐designed glyphs.