NextBestOnce: Achieving Polylog Routing despite Non-greedy Embeddings

Social Overlays suffer from high message delivery delays due to insufficient routing strategies. Limiting connections to device pairs that are owned by individuals with a mutual trust relationship in real life, they form topologies restricted to a subgraph of the social network of their users. While centralized, highly successful social networking services entail a complete privacy loss of their users, Social Overlays at higher performance represent an ideal private and censorship-resistant communication substrate for the same purpose. Routing in such restricted topologies is facilitated by embedding the social graph into a metric space. Decentralized routing algorithms have up to date mainly been analyzed under the assumption of a perfect lattice structure. However, currently deployed embedding algorithms for privacy-preserving Social Overlays cannot achieve a sufficiently accurate embedding and hence conventional routing algorithms fail. Developing Social Overlays with acceptable performance hence requires better models and enhanced algorithms, which guarantee convergence in the presence of local optima with regard to the distance to the target. We suggest a model for Social Overlays that includes inaccurate embeddings and arbitrary degree distributions. We further propose NextBestOnce, a routing algorithm that can achieve polylog routing length despite local optima. We provide analytical bounds on the performance of NextBestOnce assuming a scale-free degree distribution, and furthermore show that its performance can be improved by more than a constant factor when including Neighbor-of-Neighbor information in the routing decisions.Comment: 23 pages, 2 figure

Comprehending Kademlia Routing - A Theoretical Framework for the Hop Count Distribution

The family of Kademlia-type systems represents the most efficient and most widely deployed class of internet-scale distributed systems. Its success has caused plenty of large scale measurements and simulation studies, and several improvements have been introduced. Its character of parallel and non-deterministic lookups, however, so far has prevented any concise formal analysis. This paper introduces the first comprehensive formal model of the routing of the entire family of systems that is validated against previous measurements. It sheds light on the overall hop distribution and lookup delays of the different variations of the original protocol. It additionally shows that several of the recent improvements to the protocol in fact have been counter-productive and identifies preferable designs with regard to routing overhead and resilience.Comment: 12 pages, 6 figure

A Lightweight Approach for Improving the Lookup Performance in Kademlia-type Systems

Discovery of nodes and content in large-scale distributed systems is generally based on Kademlia, today. Understanding Kademlia-type systems to improve their performance is essential for maintaining a high service quality for an increased number of participants, particularly when those systems are adopted by latency-sensitive applications. This paper contributes to the understanding of Kademlia by studying the impact of \emph{diversifying} neighbours' identifiers within each routing table bucket on the lookup performance. We propose a new, yet backward-compatible, neighbour selection scheme that attempts to maximize the aforementioned diversity. The scheme does not cause additional overhead except negligible computations for comparing the diversity of identifiers. We present a theoretical model for the actual impact of the new scheme on the lookup's hop count and validate it against simulations of three exemplary Kademlia-type systems. We also measure the performance gain enabled by a partial deployment for the scheme in the real KAD system. The results confirm the superiority of the systems that incorporate our scheme.Comment: 13 pages, 8 figures, conference version 'Diversity Entails Improvement: A new Neighbour Selection Scheme for Kademlia-type Systems' at IEEE P2P 201

Efficient Cloud-based Secret Shuffling via Homomorphic Encryption

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers' privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases

Ray-tracing based Inference Attacks on Physical Layer Security

In wireless network security, physical layer security provides a viable alternative to classical cryptography, which deliver high security guarantees with minimal energy expenditure. Nevertheless, these cryptograhpic primitives are based on assumptions about physical conditions which in practice may not be fulfilled.In this work we present a ray-tracing based attack, which challenges the basic assumption of uncorrelated channel properties for eavesdroppers. We realize this attack and evaluate it with real world measurement, and thereby show that such attacks can predict channel properties better than previous attacks and are also more generally applicable