Mödinger, D., Lorenz, J.-H. and Hauck, F.J. 2021. Statistical privacy-preserving message broadcast for peer-to-peer networks. PLOS ONE. 16, 5 (May 2021), 1–24.
Privacy concerns are widely discussed in research and society in general. For the public infrastructure of financial blockchains, this discussion encompasses the privacy of the originator of a transaction broadcasted on the underlying peer-to-peer network. Adaptive diffusion is an approach to expose an alternative source of a message to attackers. However, this approach assumes an unsuitable attacker model and a non-realistic network model for current peer-to-peer networks on the Internet. We transform adaptive diffusion into a new statistical privacy-preserving broadcast protocol for realistic current networks. We model a class of unstructured peer-to-peer networks as organically growing graphs and provide models for other classes of such networks. We show that the distribution of shortest paths can be modelled using a normal distribution N ( μ , σ 2 ). We determine statistical estimators for μ, σ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges per node k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for maximum privacy, with participants having only limited information about network topology.
Köstler, J., Reiser, H.P., Habiger, G. and Hauck, F.J. 2021. SmartStream: towards Byzantine resilient data streaming. 36th Ann. ACM Symp. on Appl. Comp. – SAC (Virtual Event, Republic of Korea, Mar. 2021), 213–222.
Data streaming platforms connect heterogeneous services through the publish-subscribe paradigm. Currently available platforms provide protection against crash faults, but are not resistant against Byzantine faults like arbitrary hardware faults and intrusions. State machine replication can provide this protection, but the higher resource requirements and the more elaborated communication primitives usually result in a higher overall complexity and a non-negligible performance degradation. This is especially true for data streaming if the default textbook approach of integrating the service into a replicated state machine is followed without further adaptions. The standard state management with state logs and snapshots and without any partitioning scheme limits both performance and scalability in a way those systems become unusable in practice. That is why we propose SmartStream, a topic-based Byzantine fault-tolerant data streaming platform that harmonizes the competing concepts of both systems and leverages the specific characteristics of data streaming, namely the append-only semantics of the application state and its partitionable structure. We show its effectiveness in a prototype implementation and evaluate its performance. The evaluation results show a moderate drop in system throughput when compared to state-of-the-art data streaming platforms like Apache Kafka, but reasonable overall performance considering the stronger resilience guarantees.
David Mödinger, Heß, A. and Hauck, F.J. 2021. Arbitrary Length k-Anonymous Dining-Cryptographers Communication. CoRR. abs/2103.17091, (Mar. 2021).
Dining-cryptographers networks (DCN) can achieve information-theoretical privacy. Unfortunately, they are not well suited for peer-to-peer networks as they are used in blockchain applications to disseminate transactions and blocks among par- ticipants. In previous but preliminary work, we proposed a three- phase approach with an initial phase based on a DCN with a group size of k while later phases take care of the actual broadcast within a peer-to-peer network. This paper describes our DCN protocol in detail and adds a performance evaluation powered by our proof-of-concept implementation. Our contributions are (i) an extension of the DCN protocol by von Ahn for fair delivery of arbitrarily long messages sent by potentially multiple senders, (ii) a privacy and security analysis of this extension, (iii) various performance optimisation especially for best-case operation, and (iv) a performance evaluation. The latter uses a latency of 100 ms and a bandwidth limit of 50 Mbit s−1 between participants. The interquartile range of the largest test of the highly secured version took 35s ± 1.25s for a full run. All tests of the optimized common-case mode show the dissemination of a message within 0.5s ± 0.1s. These results compare favourably to previously established protocols for k-anonymous transmission of fixed size messages, outperforming the original protocol for messages as small as 2 KiB.
Heß, A., Hauck, F.J., Mödinger, D., Pietron, J., Tichy, M. and Domaschka, J. 2021. Morpheus: A Degradation Framework for Resilient IoT Systems. STAF Workshops (Virtual Event, Bergen - Norway, 2021), 105–114.
Graceful degradation is an established concept to improve the resilience of systems, especially when other resilience mechanisms have failed. Its implementation is often heavily tied to the application code and, thus, cumbersome and error prone. As IoT systems get not only ubiquitous but also critical, reliable graceful degradation would be ideal. In this paper, we present the Morpheus framework that provides a TypeScript-internal DSL to enable a systematic development of degradable IoT systems. The design of the framework is based on the concept of separation of concerns by providing distinct yet linked languages to specify hierarchical components and their connections; the components’ operating modes and transfer functions between them; as well as state machines for the specification of the components’ behaviour in each operating mode. The operating modes for each component serve as degradation levels. Automatic degradation of a component is triggered in case of failures of connected components. With recovery from underlying failures, the component is automatically upgraded back to a higher level. We illustrate our framework using a simplified prototype of an entrance barrier of a parking garage
Erb, B., Habiger, G. and Hauck, F.J. 2016. On the Potential of Event Sourcing for Retroactive Actor-based Programming. First Workshop on Programming Models and Languages for Distributed Computing (New York, NY, USA, 2016), 1–5.
The actor model is an established programming model for distributed applications. Combining event sourcing with the actor model allows the reconstruction of previous states of an actor. When this event sourcing approach for actors is enhanced with additional causality information, novel types of actor-based, retroactive computations are possible. A globally consistent state of all actors can be reconstructed retrospectively. Even retroactive changes of actor behavior, state, or messaging are possible, with partial recomputations and projections of changes in the past. We believe that this approach may provide beneficial features to actor-based systems, including retroactive bugfixing of applications, decoupled asynchronous global state reconstruction for recovery, simulations, and exploration of distributed applications and algorithms.