Auditable, Available and Resilient Private Computation on the Blockchain via MPC

Simple but mission-critical internet-based applications that require extremely high reliability, availability, and verifiability (e.g., auditability) could benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is normally publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. In this work, we investigate using MPC techniques to protect the privacy of a blockchain computation. While our main goal is to hide both the data and the computed function itself, we also consider the standard MPC setting where the function is public. We describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a blockchain MPC architecture and system. The GABLE architecture specifies the roles and capabilities of the players. GABLE includes two approaches for implementing MPC over blockchain: Garbled Circuits (GC), evaluating universal circuits, and Garbled Finite State Automata (GFSA). We formally model and prove the security of GABLE implemented over garbling schemes, a popular abstraction of GC and GFSA from (Bellare et al, CCS 2012). We analyze in detail the performance (including Ethereum gas costs) of both approaches and discuss the trade-offs. We implement a simple prototype of GABLE and report on the implementation issues and experience

Modeling and simulation for cyber-physical system security research, development and applications.

This paper describes a new hybrid modeling and simulation architecture developed at Sandia for understanding and developing protections against and mitigations for cyber threats upon control systems. It first outlines the challenges to PCS security that can be addressed using these technologies. The paper then describes Virtual Control System Environments (VCSE) that use this approach and briefly discusses security research that Sandia has performed using VCSE. It closes with recommendations to the control systems security community for applying this valuable technology

Efficient algorithms for phylogenetic post-analysis

A variety of tasks are typically performed after a phylogenetic reconstruction proper – tasks which fall under the category phylogenetic post-analysis. In this dissertation, we present novel approaches and efficient algorithms for three post-analysis tasks: taking distances between (typically, all pairs in a set of) trees, bootstrapping, and building consensus trees. For instance, it is often the case that reconstruction finds multiple plausible trees. One basic way of addressing this situation is to take distances between pairs of trees, in order to gain an understanding of the extent to which the trees disagree. The most frequently employed manner for computing the distance between a tree pair is the Robinson-Foulds metric, a natural dissimilarity measure between a pair of phylogenetic trees. We present a novel family of algorithms for efficiently computing the Robinson-Foulds metric. Bootstrapping is a post-analysis technique for drawing support values on tree edges, and is often used for assessing the extent to which the underlying data (e.g., molecular sequences) supports a reconstructed tree. The basis of the approach is to reconstruct many trees, called replicates, based on random subsampling of the original data. However, to date, there has been little treatment in phylogeny regarding the question of how many bootstrap replicates to generate. We propose bootstopping criteria which are designed to provide on-the-fly (i.e., runtime) guidance for determining when enough bootstrap replicates have been reconstructed. Another common post-analysis task is to build a consensus tree, a summary tree that attempts to capture the information agreed upon by bootstrap replicates. Unfortunately, the most popular consensus methods are susceptible to confusion by rogue taxa, i.e., taxa that cannot be placed with assurance anywhere within the tree. We present novel theory and efficient algorithms to identify rogue taxa, as well as a novel technique for interpreting the results (in the context of bootstrapping)

A sublineartime randomized approximation scheme for the Robinson-Foulds metric

Abstract. The Robinson-Foulds (RF) metric is the measure most widely used in comparing phylogenetic trees; it can be computed in linear time using Day’s algorithm. When faced with the need to compare large numbers of large trees, however, even linear time becomes prohibitive. We present a randomized approximation scheme that provides, with high probability, a (1+ε) approximation of the true RF metric for all pairs of trees in a given collection. Our approach is to use a sublinear-space embedding of the trees, combined with an application of the Johnson-Lindenstrauss lemma to approximate vector norms very rapidly. We discuss the consequences of various parameter choices (in the embedding and in the approximation requirements). We also implemented our algorithm as a Java class that can easily be combined with popular packages such as Mesquite; in consequence, we present experimental results illustrating the precision and running-time tradeoffs as well as demonstrating the speed of our approach.

Efficiently Computing the Robinson-Foulds Metric ∗

The Robinson-Foulds (RF) metric is the measure most widely used in comparing phylogenetic trees; it can be computed in linear time using Day’s algorithm. When faced with the need to compare large numbers of large trees, however, even linear time becomes prohibitive. We present a randomized approximation scheme that provides, in sublinear time and with high probability, a (1+ε) approximation of the true RF metric. Our approach is to use a sublinear-space embedding of the trees, combined with an application of the Johnson-Lindenstrauss lemma to approximate vector norms very rapidly. We complement our algorithm by presenting an efficient embedding procedure, thereby resolving an open issue from the preliminary version of this paper. We have also improved the performance of Day’s (exact) algorithm in practice by using techniques discovered while implementing our approximation scheme. Indeed, we give a unified framework for edge-based tree algorithms in which implementation tradeoffs are clear. Finally, we present detailed experimental results illustrating the precision and running-time tradeoffs as well as demonstrating the speed of our approach. Our new implementation, FastRF, is available as an open-source tool for phylogenetic analysis.