Benjamin Schiller

Measuring Freenet in the Wild: Censorship-Resilience under Observation

Freenet, a fully decentralized publication system designed for censorship-resistant communication, exhibits long delays and low success rates for finding and retrieving content. In order to improve its performance, an in-depth understanding of the deployed system is required. Therefore, we performed an extensive measurement study accompanied by a code analysis to identify bottlenecks of the existing algorithms and obtained a realistic user model for the improvement and evaluation of new algorithms.

GTNA: a framework for the graph-theoretic network analysis

Concise and reliable graph-theoretic analysis of complex networks today is a cumbersome task, consisting essentially of the adaptation of intricate libraries for each specific problem instance. The growing number of complex metrics that have been proposed in the last years, which mainly gain significance due to the increasing computational capabilities at hand, have led to important new insights in the field. However, they have solely been implemented as single algorithms, each specialized for the purpose of calculating exactly the targeted metric for a selected type of network graph. A comprehensive, extensible tool for the concise evaluation of graphs is currently not available. For this purpose we introduce the Graph-Theoretic Network Analyzer (GTNA), an efficient, Java-based toolkit for the comprehensive analysis of complex network graphs. GTNA, while already including the main metrics that are used to analyze the complex networks in computer science today, is simple to extend through a well defined plugin interface for metrics, network descriptions and network generator models. Throughout the paper we present the design and simple extensibility of GTNA, as well as the network models and metrics that are already implemented and give examples of its scalability and performance.

Attack Resistant Network Embeddings for Darknets

Darknets, connecting only devices between participants of mutual trust in the real world, rely on cooperative, precise, and attack resistant embeddings to evolve routing structures on the name space. Only precise embeddings allow for performant communication with low overhead on these networks. With Darknets being deployed in generally untrusted, even adverse environments, external or internal attacks have to be assumed commonplace. Their impact hence has to be limited and the embedding must be made resistant against even sophisticated attacks. Analysing Dark Freenet, the only current approach implementing a full Dark net, we devise simple attacks that render its embedding entirely corrupt. In response we derive a novel embedding that is based on local decisions only, and which not only is resistant to such attacks, but additionally outperforms the Dark Freenet in terms of precision.

StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs

Determining the occurrence of motifs yields profound insight for many biological systems, like metabolic, protein-protein interaction, and protein structure networks. Meaningful spatial protein-structure motifs include enzyme active sites and ligand-binding sites which are essential for function, shape, and performance of an enzyme. Analyzing their dynamics over time leads to a better understanding of underlying properties and processes. In this work, we present StreaM, a stream-based algorithm for counting undirected 4-vertex motifs in dynamic graphs. We evaluate StreaM against the four predominant approaches from the current state of the art on generated and real-world datasets, a simulation of a highly dynamic enzyme. For this case, we show that StreaM is capable to capture essential molecular protein dynamics and thereby provides a powerful method for evaluating large molecular dynamics trajectories. Compared to related work, our approach achieves speedups of upi¾?to 2,i¾?300 times on real-world datasets.

Demo overview: fully decentralised authentication scheme for ICN in disaster scenarios

Self-certifying names provide the property that any entity in a distributed system can verify the binding between a corresponding public key and the self-certifying name without relying on a trusted third party. However, self-certifying names lack a binding with a corresponding real-world identity. In this demonstration, we present the implementation of a concrete mechanism for using a Web-of-Trust in conjunction with self-certifying names to provide this missing binding. Our prototype runs on Android devices and demonstrates a decentralised message authentication scheme for any kind of content-oriented architecture. In the demonstration, we show how our proposed scheme performs---in terms of time needed to assess the trustworthiness of information retrieved---in a fully decentralised scenario: fragmented (mobile) networks. In such a scenario, connectivity to centralized authentication entities and Web-of-Trust key-servers is not available. Our scheme is hence executed solely on end-user terminals itself (which have limited processing capabilities).

Content Virality on Online Social Networks: Empirical Evidence from Twitter, Facebook, and Google+ on German News Websites

The virality of content describes its likelihood to be shared with peers. In this work, we investigate how content characteristics impact the sharing likelihood of news articles on Twitter, Facebook, and Google+. We examine a random sample of 4,278 articles from the most popular news websites in Germany categorized by human classifiers and text mining tools. Our analysis reveals commonalities and subtle differences between the three networks indicating different sharing patterns of their users.

Efficient Data Structures for Dynamic Graph Analysis

In the era of social networks, gene sequencing, and big data, a new class of applications that analyze the properties of large graphs as they dynamically change over time has emerged. The performance of these applications is highly influenced by the data structures used to store and access the graph in memory. Depending on its size and structure, update frequency, and read accesses of the analysis, the use of different data structures can yield great performance variations. Even for expert programmers, it is not always obvious, which data structure is the best choice for a given scenario. In this paper, we present a framework for handling this issue automatically. It provides compile-time support for automatically selecting the most efficient data structures for a given graph analysis application assuming a consistent workload on the graph. We perform a measurement study to better understand the performance of five data structures and evaluate a prototype Java implementation of our framework. It achieves a speedup of up to 4.7× compared to basic data structure configurations for the analysis of real-world dynamic graphs.

StreAM-T_g: Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

In this work, we present a new coarse grained representation of RNA dynamics. It is based on cliques and their patterns within adjacency matrices obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. Each adjacency matrix represents the interactions of k nucleotides. We then define transitions between states as changes in the adjacency matrices which form a Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-\(T_g\), a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. Here, we benchmark StreAM-\(T_g\) (a) for random and RNA unit sphere dynamic graphs. (b) we apply our method on a long term molecular dynamics simulation of a synthetic riboswitch (1,000 ns). In the light of experimental data our results show important design opportunities for the riboswitch.

CoMon: A system architecture for improving caching in CCN

Content-Centric Networking (CCN) promises to yield large efficiency gains for Internet content distribution. Its autonomous cache management, however, raises doubts about achieving the intended goals optimally. A coordinated cache management, based on timely usage information, will help to fully leverage the cache efficiency. In this poster we introduce CoMon, a system architecture that implements Coordinated caching based on Monitoring of content usage and its stability. CoMon aims at improving CCN caching with low monitoring and communication overheads.

StreaMD: Advanced analysis of molecular dynamics using R.

Gromacs is one of the most popular molecular simulation suites currently available. In this contribution we present streaMD, the first interface between Gromacs trajectory files and the statistical language R. The amount of data created due to ever increasing computational power renders fast and efficient analysis of trajectories into a challenge. Especially as standard approaches such as root‐mean square fluctuations and the like provide only limited physical insight. In our streaMD package integration of the Gromacs I/O libraries with advanced, graph‐based analysis methods as the java library Stream leads to both: improved speed and analysis depth. We benchmark our results and highlight the applicability of the package by an interesting problem in RNA design, namely the interaction of tetracycline with an aptamer.