Showing papers by "SRI International published in 2017"

The EcoCyc database: reflecting new knowledge about Escherichia coli K-12.

Abstract: EcoCyc (EcoCyc.org) is a freely accessible, comprehensive database that collects and summarizes experimental data for Escherichia coli K-12, the best-studied bacterial model organism. New experimental discoveries about gene products, their function and regulation, new metabolic pathways, enzymes and cofactors are regularly added to EcoCyc. New SmartTable tools allow users to browse collections of related EcoCyc content. SmartTables can also serve as repositories for user- or curator-generated lists. EcoCyc now supports running and modifying E. coli metabolic models directly on the EcoCyc website.

Highly efficient electrocaloric cooling with electrostatic actuation

Abstract: Solid-state refrigeration offers potential advantages over traditional cooling systems, but few devices offer high specific cooling power with a high coefficient of performance (COP) and the ability to be applied directly to surfaces. We developed a cooling device with a high intrinsic thermodynamic efficiency using a flexible electrocaloric (EC) polymer film and an electrostatic actuation mechanism. Reversible electrostatic forces reduce parasitic power consumption and allow efficient heat transfer through good thermal contacts with the heat source or heat sink. The EC device produced a specific cooling power of 2.8 watts per gram and a COP of 13. The new cooling device is more efficient and compact than existing surface-conformable solid-state cooling technologies, opening a path to using the technology for a variety of practical applications.

Effective inhibition of MERS-CoV infection by resveratrol

Abstract: Middle East Respiratory Syndrome coronavirus (MERS-CoV) is an emerging viral pathogen that causes severe morbidity and mortality. Up to date, there is no approved or licensed vaccine or antiviral medicines can be used to treat MERS-CoV-infected patients. Here, we analyzed the antiviral activities of resveratrol, a natural compound found in grape seeds and skin and in red wine, against MERS-CoV infection. We performed MTT and neutral red uptake assays to assess the survival rates of MERS-infected Vero E6 cells. In addition, quantitative PCR, western blotting, and immunofluorescent assays determined the intracellular viral RNA and protein expression. For viral productivity, we utilized plaque assays to confirm the antiviral properties of resveratrol against MERS-CoV. Resveratrol significantly inhibited MERS-CoV infection and prolonged cellular survival after virus infection. We also found that the expression of nucleocapsid (N) protein essential for MERS-CoV replication was decreased after resveratrol treatment. Furthermore, resveratrol down-regulated the apoptosis induced by MERS-CoV in vitro. By consecutive administration of resveratrol, we were able to reduce the concentration of resveratrol while achieving inhibitory effectiveness against MERS-CoV. In this study, we first demonstrated that resveratrol is a potent anti-MERS agent in vitro. We perceive that resveratrol can be a potential antiviral agent against MERS-CoV infection in the near future.

Executive Functions, Memory, and Social Cognitive Deficits and Recovery in Chronic Alcoholism: A Critical Review to Inform Future Research

Abstract: Alcoholism is a complex and dynamic disease, punctuated by periods of abstinence and relapse, and influenced by a multitude of vulnerability factors. Chronic excessive alcohol consumption is associated with cognitive deficits, ranging from mild to severe, in executive functions, memory, and metacognitive abilities, with associated impairment in emotional processes and social cognition. These deficits can compromise efforts in initiating and sustaining abstinence by hampering efficacy of clinical treatment and can obstruct efforts in enabling good decision making success in interpersonal/social interactions, and awareness of cognitive and behavioral dysfunctions. Despite evidence for differences in recovery levels of selective cognitive processes, certain deficits can persist even with prolonged sobriety. Herein is presented a review of alcohol-related cognitive impairments affecting component processes of executive functioning, memory, and the recently investigated cognitive domains of metamemory, social cognition, and emotional processing; also considered are trajectories of cognitive recovery with abstinence. Finally, in the spirit of critical review, limitations of current knowledge are noted and avenues for new research efforts are proposed that focus on (i) the interaction among emotion-cognition processes and identification of vulnerability factors contributing to the development of emotional and social processing deficits and (ii) the time line of cognitive recovery by tracking alcoholism's dynamic course of sobriety and relapse. Knowledge about the heterochronicity of cognitive recovery in alcoholism has the potential of indicating at which points during recovery intervention may be most beneficial.

The version 3 OMI NO 2 standard product

Abstract: . We describe the new version 3.0 NASA Ozone Monitoring Instrument (OMI) standard nitrogen dioxide (NO2) products (SPv3). The products and documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center ( https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/ ). The major improvements include (1) a new spectral fitting algorithm for NO2 slant column density (SCD) retrieval and (2) higher-resolution (1° latitude and 1.25° longitude) a priori NO2 and temperature profiles from the Global Modeling Initiative (GMI) chemistry–transport model with yearly varying emissions to calculate air mass factors (AMFs) required to convert SCDs into vertical column densities (VCDs). The new SCDs are systematically lower (by ∼ 10–40 %) than previous, version 2, estimates. Most of this reduction in SCDs is propagated into stratospheric VCDs. Tropospheric NO2 VCDs are also reduced over polluted areas, especially over western Europe, the eastern US, and eastern China. Initial evaluation over unpolluted areas shows that the new SPv3 products agree better with independent satellite- and ground-based Fourier transform infrared (FTIR) measurements. However, further evaluation of tropospheric VCDs is needed over polluted areas, where the increased spatial resolution and more refined AMF estimates may lead to better characterization of pollution hot spots.

Measuring Student Learning in Introductory Block-Based Programming: Examining Misconceptions of Loops, Variables, and Boolean Logic

Abstract: Programming in block-based environments is a key element of introductory computer science (CS) curricula in K-12 settings. Past research conducted in the context of text-based programming points to several challenges related to novice learners' understanding of foundational programming constructs such as variables, loops, and expressions. This research aims to develop assessment items for measuring student understanding in introductory CS classrooms in middle school using a principled approach for assessment design. This paper describes the design of assessments items that were piloted with 100 6th, 7th, 8th graders who had completed an introductory programming course using Scratch. The results and follow-up cognitive thinkalouds indicate that students are generally unfamiliar with the use of variables, and harbor misconceptions about them. They also have trouble with other aspects of introductory programming such as how loops work, and how the Boolean operators work. These findings point to the need for pedagogy that combines popular constructionist activities with those that target conceptual learning, along with better professional development to support teachers' conceptual learning of these foundational constructs.

CRYSTALS - Kyber: A CCA-secure Module-Lattice-Based KEM

Abstract: Rapid advances in quantum computing, together with the announcement by the National Institute of Standards and Technology (NIST) to define new standards for digital-signature, encryption, and key-establishment protocols, have created significant interest in post-quantum cryptographic schemes. This paper introduces Kyber (part of CRYSTALS - Cryptographic Suite for Algebraic Lattices - a package submitted to NIST post-quantum standardization effort in November 2017), a portfolio of post-quantum cryptographic primitives built around a key-encapsulation mechanism (KEM), based on hardness assumptions over module lattices. Our KEM is most naturally seen as a successor to the NEWHOPE KEM (Usenix 2016). In particular, the key and ciphertext sizes of our new construction are about half the size, the KEM offers CCA instead of only passive security, the security is based on a more general (and flexible) lattice problem, and our optimized implementation results in essentially the same running time as the aforementioned scheme. We first introduce a CPA-secure public-key encryption scheme, apply a variant of the Fujisaki-Okamoto transform to create a CCA-secure KEM, and eventually construct, in a black-box manner, CCA-secure encryption, key exchange, and authenticated-key-exchange schemes. The security of our primitives is based on the hardness of Module-LWE in the classical and quantum random oracle models, and our concrete parameters conservatively target more than 128 bits of post-quantum security.

RNA sensors of the innate immune system and their detection of pathogens

Abstract: The innate immune system plays a critical role in pathogen recognition and initiation of protective immune response through the recognition of pathogen associated molecular patterns (PAMPs) by its pattern recognition receptors (PRRs). Nucleic acids including RNA and DNA have been recognized as very important PAMPs of pathogens especially for viruses. RNA are the major PAMPs of RNA viruses, to which most severe disease causing viruses belong thus posing a tougher challenge to human and animal health. Therefore, the understanding of the immune biology of RNA PRRs is critical for control of pathogen infections especially for RNA virus infections. RNA PRRs are comprised of TLR3, TLR7, TLR8, RIG-I, MDA5, NLRP3, NOD2, and some other minorities. This review introduces these RNA PRRs by describing the cellular localizations, ligand recognitions, activation mechanisms, cell signaling pathways, and recognition of pathogens; the cross-talks between various RNA PRRs are also reviewed. The deep insights of these RNA PRRs can be utilized to improve anti-viral immune response. © 2017 IUBMB Life, 69(5):297-304, 2017.

AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos

Abstract: We propose a novel method for temporally pooling frames in a video for the task of human action recognition. The method is motivated by the observation that there are only a small number of frames which, together, contain sufficient information to discriminate an action class present in a video, from the rest. The proposed method learns to pool such discriminative and informative frames, while discarding a majority of the non-informative frames in a single temporal scan of the video. Our algorithm does so by continuously predicting the discriminative importance of each video frame and subsequently pooling them in a deep learning framework. We show the effectiveness of our proposed pooling method on standard benchmarks where it consistently improves on baseline pooling methods, with both RGB and optical flow based Convolutional networks. Further, in combination with complementary video representations, we show results that are competitive with respect to the state-of-the-art results on two challenging and publicly available benchmark datasets.

Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking

Abstract: Objective:The authors sought evidence for altered adolescent brain growth trajectory associated with moderate and heavy alcohol use in a large national, multisite, prospective study of adolescents before and after initiation of appreciable alcohol use.Method:This study examined 483 adolescents (ages 12–21) before initiation of drinking and 1 and 2 years later. At the 2-year assessment, 356 participants continued to meet the study’s no/low alcohol consumption entry criteria, 65 had initiated moderate drinking, and 62 had initiated heavy drinking. MRI was used to quantify regional cortical and white matter volumes. Percent change per year (slopes) in adolescents who continued to meet no/low criteria served as developmental control trajectories against which to compare those who initiated moderate or heavy drinking.Results:In no/low drinkers, gray matter volume declined throughout adolescence and slowed in many regions in later adolescence. Complementing gray matter declines, white matter regions grew at fas...

The future of the internet of things

Abstract: The IoT can become ubiquitous worldwide---if the pursuit of systemic trustworthiness can overcome the potential risks.

Attention-Based Extraction of Structured Information from Street View Imagery

Abstract: We present a neural network model — based on Convolutional Neural Networks, Recurrent Neural Networks and a novel attention mechanism — which achieves 84.2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72.46%. Furthermore, our new method is much simpler and more general than the previous approach. To demonstrate the generality of our model, we show that it also performs well on an even more challenging dataset derived from Google Street View, in which the goal is to extract business names from store fronts. Finally, we study the speed/accuracy tradeoff that results from using CNN feature extractors of different depths. Surprisingly, we find that deeper is not always better (in terms of accuracy, as well as speed). Our resulting model is simple, accurate and fast, allowing it to be used at scale on a variety of challenging real-world text extraction problems.

Evidence-Centered Assessment Design

Abstract: Design patterns are tools to support task authoring under an evidence-centered approach to assessment design (ECD). This chapter reviews the basic concepts of ECD, focusing on evidentiary arguments. It defines the attributes of design patterns, and shows the roles they play in creating tasks around valid assessment arguments.

Tumor-associated B-cells induce tumor heterogeneity and therapy resistance

Abstract: In melanoma, therapies with inhibitors to oncogenic BRAFV600E are highly effective but responses are often short-lived due to the emergence of drug-resistant tumor subpopulations. We describe here a mechanism of acquired drug resistance through the tumor microenvironment, which is mediated by human tumor-associated B cells. Human melanoma cells constitutively produce the growth factor FGF-2, which activates tumor-infiltrating B cells to produce the growth factor IGF-1. B-cell-derived IGF-1 is critical for resistance of melanomas to BRAF and MEK inhibitors due to emergence of heterogeneous subpopulations and activation of FGFR-3. Consistently, resistance of melanomas to BRAF and/or MEK inhibitors is associated with increased CD20 and IGF-1 transcript levels in tumors and IGF-1 expression in tumor-associated B cells. Furthermore, first clinical data from a pilot trial in therapy-resistant metastatic melanoma patients show anti-tumor activity through B-cell depletion by anti-CD20 antibody. Our findings establish a mechanism of acquired therapy resistance through tumor-associated B cells with important clinical implications.

DELTA: A Security Assessment Framework for Software-Defined Networks

Abstract: Developing a systematic understanding of the attack surface of emergent networks, such as software-defined networks (SDNs), is necessary and arguably the starting point toward making it more secure. Prior studies have largely relied on ad hoc empirical methods to evaluate the security of various SDN elements from different perspectives. However, they have stopped short of converging on a systematic methodology or developing automated systems to rigorously test for security flaws in SDNs. Thus, conducting security assessments of new SDN software remains a non-replicable and unregimented process. This paper makes the case for automating and standardizing the vulnerability identification process in SDNs. As a first step, we developed a security assessment framework, DELTA, that reinstantiates published SDN attacks in diverse test environments. Next, we enhanced our tool with a protocol-aware fuzzing module to automatically discover new vulnerabilities. In our evaluation, DELTA successfully reproduced 20 known attack scenarios across diverse SDN controller environments and discovered seven novel SDN application mislead attacks.

Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment

Abstract: Learner modeling has been used in computer-based learning environments to model learners' domain knowledge, cognitive skills, and interests, and customize their experiences in the environment based on this information. In this paper, we develop a learner modeling and adaptive scaffolding framework for Computational Thinking using Simulation and Modeling (CTSiM)--an open ended learning environment that supports synergistic learning of science and Computational Thinking (CT) for middle school students. In CTSiM, students have the freedom to choose and coordinate use of the different tools provided in the environment, as they build and test their models. However, the open-ended nature of the environment makes it hard to interpret the intent of students' actions, and to provide useful feedback and hints that improves student understanding and helps them achieve their learning goals. To address this challenge, we define an extended learner modeling scheme that uses (1) a hierarchical task model for the CTSiM environment, (2) a set of strategies that support effective learning and model building, and (3) effectiveness and coherence measures that help us evaluate student's proficiency in the different tasks and strategies. We use this scheme to dynamically scaffold learners when they are deficient in performing their tasks, or they demonstrate suboptimal use of strategies. We demonstrate the effectiveness of our approach in a classroom study where one group of 6th grade students received scaffolding and the other did not. We found that students who received scaffolding built more accurate models, used modeling strategies effectively, adopted more useful modeling behaviors, showed a better understanding of important science and CT concepts, and transferred their modeling skills better to new scenarios.

Mutant U2AF1-expressing cells are sensitive to pharmacological modulation of the spliceosome

Abstract: Somatic mutations in spliceosome genes are detectable in ∼50% of patients with myelodysplastic syndromes (MDS). We hypothesize that cells harbouring spliceosome gene mutations have increased sensitivity to pharmacological perturbation of the spliceosome. We focus on mutant U2AF1 and utilize sudemycin compounds that modulate pre-mRNA splicing. We find that haematopoietic cells expressing mutant U2AF1(S34F), including primary patient cells, have an increased sensitivity to in vitro sudemycin treatment relative to controls. In vivo sudemycin treatment of U2AF1(S34F) transgenic mice alters splicing and reverts haematopoietic progenitor cell expansion induced by mutant U2AF1 expression. The splicing effects of sudemycin and U2AF1(S34F) can be cumulative in cells exposed to both perturbations-drug and mutation-compared with cells exposed to either alone. These cumulative effects may result in downstream phenotypic consequences in sudemycin-treated mutant cells. Taken together, these data suggest a potential for treating haematological cancers harbouring U2AF1 mutations with pre-mRNA splicing modulators like sudemycins.

A theory of formal synthesis via inductive learning

Abstract: Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.

Attention-based Extraction of Structured Information from Street View Imagery

Abstract: We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84.2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72.46%. Furthermore, our new method is much simpler and more general than the previous approach. To demonstrate the generality of our model, we show that it also performs well on an even more challenging dataset derived from Google Street View, in which the goal is to extract business names from store fronts. Finally, we study the speed/accuracy tradeoff that results from using CNN feature extractors of different depths. Surprisingly, we find that deeper is not always better (in terms of accuracy, as well as speed). Our resulting model is simple, accurate and fast, allowing it to be used at scale on a variety of challenging real-world text extraction problems.

ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing

Abstract: We present $ADDSEN$ middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. $ADDSEN$ uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented $ADDSEN$ in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocols in both single-swarm and multi-swarm cases.

Flow Wars: Systemizing the Attack Surface and Defenses in Software-Defined Networks

Abstract: Emerging software defined network (SDN) stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.

Toward an Evolving Conceptualization of Instructional Leadership as Leadership for Learning: Meta-narrative Review of 109 Quantitative Studies across 25 Years.

Abstract: Purpose Instructional leadership has been an active area of educational administration research over the past 30 years. However, there has been significant divergence in how instructional leadership has been conceptualized over time. The purpose of this paper is to present a comprehensive review of 25 years of quantitative instructional leadership research, up through 2013, using a nationally generalizable data set. Design/methodology/approach The authors conducted a meta-narrative review of 109 studies that investigated at least one aspect of instructional leadership using the Schools and Staffing Survey (SASS) administered by the US National Center for Education Statistics. Findings There were four major themes of instructional leadership research that analyzed SASS data: principal leadership and influence, teacher autonomy and influence, adult development, and school climate. The three factors most researched in relationship to instructional leadership themes were: teacher satisfaction, teacher commitment, and teacher retention. This study details the major findings within each theme, describes the relationships between all seven factors, and integrates the relationships into a single model. Originality/value This paper provides the most comprehensive literature review to-date of quantitative findings investigating instructional leadership from the same nationally generalizable data set. This paper provides evidence that leadership for learning is the conceptual evolution of 25 years of diverse instructional leadership research.

Downregulation of Human Endogenous Retrovirus Type K (HERV-K) Viral env RNA in Pancreatic Cancer Cells Decreases Cell Proliferation and Tumor Growth.

Abstract: Purpose: We investigated the role of the human endogenous retrovirus type K (HERV-K) envelope (env) gene in pancreatic cancer.Experimental Design: shRNA was employed to knockdown (KD) the expression of HERV-K in pancreatic cancer cells.Results: HERV-K env expression was detected in seven pancreatic cancer cell lines and in 80% of pancreatic cancer patient biopsies, but not in two normal pancreatic cell lines or uninvolved normal tissues. A new HERV-K splice variant was discovered in several pancreatic cancer cell lines. Reverse transcriptase activity and virus-like particles were observed in culture media supernatant obtained from Panc-1 and Panc-2 cells. HERV-K viral RNA levels and anti-HERV-K antibody titers were significantly higher in pancreatic cancer patient sera (N = 106) than in normal donor sera (N = 40). Importantly, the in vitro and in vivo growth rates of three pancreatic cancer cell lines were significantly reduced after HERV-K KD by shRNA targeting HERV-K env, and there was reduced metastasis to lung after treatment. RNA-Seq results revealed changes in gene expression after HERV-K env KD, including RAS and TP53. Furthermore, downregulation of HERV-K Env protein expression by shRNA also resulted in decreased expression of RAS, p-ERK, p-RSK, and p-AKT in several pancreatic cancer cells or tumors.Conclusions: These results demonstrate that HERV-K influences signal transduction via the RAS-ERK-RSK pathway in pancreatic cancer. Our data highlight the potentially important role of HERV-K in tumorigenesis and progression of pancreatic cancer, and indicate that HERV-K viral proteins may be attractive biomarkers and/or tumor-associated antigens, as well as potentially useful targets for detection, diagnosis, and immunotherapy of pancreatic cancer. Clin Cancer Res; 23(19); 5892-911. ©2017 AACR.

Parent-Mediated Intervention for One-Year-Olds Screened as At-Risk for Autism Spectrum Disorder: A Randomized Controlled Trial.

Abstract: Theoretically, interventions initiated with at-risk infants prior to the point in time a definitive autism spectrum disorder (ASD) diagnosis can be made will improve outcomes. Pursuing this idea, we tested the efficacy of a parent-mediated early intervention called Adapted Responsive Teaching (ART) via a randomized controlled trial with 87 one-year-olds identified by community screening with the First Year Inventory as at-risk of later ASD diagnoses. We found minimal evidence for main effects of ART on child outcomes. However, ART group parents showed significantly greater increases in responsiveness to their infants than control group parents. Further, significant indirect (mediation) effects of assignment group on multiple child outcomes through changes in parent responsiveness supported our theory of change.

Lightweight Swarm Attestation: A Tale of Two LISA-s

Abstract: In the last decade, Remote Attestation (RA) emerged as a distinct security service for detecting attacks on embedded devices, cyber-physical systems (CPS) and Internet of Things (IoT) devices. RA involves verification of current internal state of an untrusted remote hardware platform (prover) by a trusted entity (verifier). RA can help the latter establish a static or dynamic root of trust in the prover and can also be used to construct other security services, such as software updates and secure deletion. Various RA techniques with different assumptions, security features and complexities, have been proposed for the single-prover scenario. However, the advent of IoT brought about the paradigm of many interconnected devices, thus triggering the need for efficient collective attestation of a (possibly mobile) group or swarm of provers. Though recent work has yielded some initial concepts for swarm attestation, several key issues remain unaddressed, and practical realizations have not been explored. This paper's main goal is to advance swarm attestation by bringing it closer to reality. To this end, it makes two contributions: (1) a new metric, called QoSA: Quality of Swarm Attestation, that captures the information offered by a swarm attestation technique; this allows comparing efficacy of multiple protocols, and (2) two practical attestation protocols -- called LISAa and LISAs -- for mobile swarms, with different QoSA features and communication and computation complexities. Security of proposed protocols is analyzed and their performance is assessed based on experiments with prototype implementations.

Zeroizing Attacks on Indistinguishability Obfuscation over CLT13

Abstract: In this work, we describe a new polynomial-time attack on the multilinear maps of Coron, Lepoint, and Tibouchi (CLT13), when used in candidate indistinguishability obfuscation (iO) schemes. More specifically, we show that given the obfuscation of the simple branching program that computes the always zero functionality previously considered by Miles, Sahai and Zhandry (Crypto 2016), one can recover the secret parameters of CLT13 in polynomial time via an extension of the zeroizing attack of Coron et al. (Crypto 2015). Our attack is generalizable to arbitrary oblivious branching programs for arbitrary functionality, and allows (1) to recover the secret parameters of CLT13, and then (2) to recover the randomized branching program entirely. Our analysis thus shows that almost all single-input variants of iO over CLT13 are insecure.