Biblio

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar

Memory efficiency and locality have substantial impact on the performance of programs, particularly when operating on large data sets. Thus, memory- or I/O-efficient algorithms have received significant attention both in theory and practice. The widespread deployment of multicore machines, however, brings new challenges. Specifically, since the memory (RAM) is shared across multiple processes, the effective memory-size allocated to each process fluctuates over time. This paper presents techniques for designing and analyzing algorithms in a cache-adaptive setting, where the RAM available to the algorithm changes over time. These techniques make analyzing algorithms in the cache-adaptive model almost as easy as in the external memory, or DAM model. Our techniques enable us to analyze a wide variety of algorithms — Master-Method-style algorithms, Akra-Bazzi-style algorithms, collections of mutually recursive algorithms, and algorithms, such as FFT, that break problems of size N into subproblems of size Theta(Nc). We demonstrate the effectiveness of these techniques by deriving several results: 1. We give a simple recipe for determining whether common divide-and-conquer cache-oblivious algorithms are optimally cache adaptive. 2. We show how to bound an algorithm's non-optimality. We give a tight analysis showing that a class of cache-oblivious algorithms is a logarithmic factor worse than optimal. 3. We show the generality of our techniques by analyzing the cache-oblivious FFT algorithm, which is not covered by the above theorems. Nonetheless, the same general techniques can show that it is at most O(loglog N) away from optimal in the cache adaptive setting, and that this bound is tight. These general theorems give concrete results about several algorithms that could not be analyzed using earlier techniques. For example, our results apply to Fast Fourier Transform, matrix multiplication, Jacobi Multipass Filter, and cache-oblivious dynamic-programming algorithms, such as Longest Common Subsequence and Edit Distance. Our results also give algorithm designers clear guidelines for creating optimally cache-adaptive algorithms.

With the increased level of distributed generation and demand response comes the need for associated mechanisms that can perform well in the face of increasingly complex deregulated energy market structures. Using Lagrangian duality theory, we develop a decentralized market mechanism that ensures that, under the guidance of a market operator, self-interested market participants: generation companies (GenCos), distribution companies (DistCos), and transmission companies (TransCos), reach a competitive equilibrium. We show that even in the presence of informational asymmetries and nonlinearities (such as power losses and transmission constraints), the resulting competitive equilibrium is Pareto efficient.

McEliece is a public-key cryptosystem based on error correcting codes. It has the ability to resist quantum-computer attacks which can break different modern public key cryptosystems such as RSA. Further more, it's encryption and decryption are very fast and have good characteristics for data parallel processing. Nowadays, modern graphic processing units (GPUs) are available in almost all hardware platforms. GPUs can comprise many compute cores which can process a huge data in parallel. In this paper, different implementations of McEliece cryptosystem are explored on NVIDIA GTX780 GPU using OpenCL framework. Our implementation results show that GPU is 331x faster than CPU when apply local memory with vector data-type to encrypt 216 messages.

The development process of short-range underwater communication systems consists of different phases. Each phase comprises a multitude of specific requirements to the development platform. Typically, the utilized hardware and software is custom-built for each phase and wireless technology. Thus, the available platforms are usually not flexible and only usable for a single development phase or a single wireless technology. Furthermore, the modification and adaption between the phases and technologies are costly and time-consuming. Platforms providing the flexibility to switch between phases or even wireless technologies are either expensive or are not suitable to be integrated into underwater equipment. We developed a flexible and modular platform consisting of a controller and different front ends. The platform is capable of performing complex tasks during all development phases. To achieve high performance with more complex modulation schemes, we combine an embedded Linux processor with a field programmable gate array (FPGA) for computational demanding tasks. We show that our platform is capable of supporting the development of short-range underwater communication systems using a variety of wireless underwater communication technologies.

The threat from insiders is an ever-growing concern for organisations, and in recent years the harm that insiders pose has been widely demonstrated. This paper describes our recent work into how we might support insider threat detection when actions are taken which can be immediately determined as of concern because they fall into one of two categories: they violate a policy which is specifically crafted to describe behaviours that are highly likely to be of concern if they are exhibited, or they exhibit behaviours which follow a pattern of a known insider threat attack. In particular, we view these concerning actions as something that we can design and implement tripwires within a system to detect. We then orchestrate these tripwires in conjunction with an anomaly detection system and present an approach to formalising tripwires of both categories. Our intention being that by having a single framework for describing them, alongside a library of existing tripwires in use, we can provide the community of practitioners and researchers with the basis to document and evolve this common understanding of tripwires.

To prevent unauthorized parties from accessing data stored on their smartphones, users have the option of enabling a "lock screen" that requires a secret code (e.g., PIN, drawing a pattern, or biometric) to gain access to their devices. We present a detailed analysis of the smartphone locking mechanisms currently available to billions of smartphone users worldwide. Through a month-long field study, we logged events from a panel of users with instrumented smartphones (N=134). We are able to show how existing lock screen mechanisms provide users with distinct tradeoffs between usability (unlocking speed vs. unlocking frequency) and security. We find that PIN users take longer to enter their codes, but commit fewer errors than pattern users, who unlock more frequently and are very prone to errors. Overall, PIN and pattern users spent the same amount of time unlocking their devices on average. Additionally, unlock performance seemed unaffected for users enabling the stealth mode for patterns. Based on our results, we identify areas where device locking mechanisms can be improved to result in fewer human errors – increasing usability – while also maintaining security.

In international military coalitions, situation awareness is achieved by gathering critical intel from different authorities. Authorities want to retain control over their data, as they are sensitive by nature, and, thus, usually employ their own authorization solutions to regulate access to them. In this paper, we highlight that harmonizing authorization solutions at the coalition level raises many challenges. We demonstrate how we address authorization challenges in the context of a scenario defined by military experts using a prototype implementation of SAFAX, an XACML-based architectural framework tailored to the development of authorization services for distributed systems.

In today's computerized and information-based society, individuals are constantly presented with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To extract value from these large, multi-domain pools of text, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their fine-grained types (e.g., people, product and food) in a scalable way. Since these methods do not rely on annotated data, predefined typing schema or hand-crafted features, they can be quickly adapted to a new domain, genre and language. We demonstrate on real datasets including various genres (e.g., news articles, discussion forum posts, and tweets), domains (general vs. bio-medical domains) and languages (e.g., English, Chinese, Arabic, and even low-resource languages like Hausa and Yoruba) how these typed entities aid in knowledge discovery and management.

Today, cloud vendors host third party black-box services, whose developers usually provide only textual descriptions or purely syntactical interface specifications. Cloud vendors that give substantial support to other third party developers to integrate hosted services into new software solutions would have a unique selling feature over their competitors. However, to reliably determine if a service is reusable, comprehensive service specifications are needed. Characteristic for comprehensive in contrast to syntactical specifications are the formalization of ontological and behavioral semantics, homogeneity according to a global ontology, and a service grounding that links the abstract service description and its technical realization. Homogeneous, semantical specifications enable to reliably identify reusable services, whereas the service grounding is needed for the technical service integration. In general, comprehensive specifications are not available and have to be derived. Existing automatized approaches are restricted to certain characteristics of comprehensiveness. In my PhD, I consider an automatized approach to derive fully-fledged comprehensive specifications for black-box services. Ontological semantics are derived from syntactical interface specifications. Behavioral semantics are mined from call logs that cloud vendors create to monitor the hosted services. The specifications are harmonized over a global ontology. The service grounding is established using traceability information. The approach enables third party developers to compose services into complex systems and creates new sales channels for cloud and service providers.

Intel SGX is the latest processor architecture promising secure code execution despite large, complex and hence potentially vulnerable legacy operating systems (OSs). However, two recent works identified vulnerabilities that allow an untrusted management OS to extract secret information from Intel SGX's enclaves, and to violate their integrity by exploiting concurrency bugs. In this work, we re-investigate delayed preemption (DP) in the context of Intel SGX. DP is a mechanism originally proposed for L4-family microkernels as disable-interrupt replacement. Recapitulating earlier results on language-based information-flow security, we illustrate the construction of leakage-free code for enclaves. However, as long as adversaries have fine-grained control over preemption timing, these solutions are impractical from a performance/complexity perspective. To overcome this, we resort to delayed preemption, and sketch a software implementation for hypervisors providing enclaves as well as a hardware extension for systems like SGX. Finally, we illustrate how static analyses for SGX may be extended to check confidentiality of preemption-delaying programs.

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.

Whether it is for conditional statement, constant, opaque predicate or equation obfuscation, Mixed Boolean Arithmetics (MBA) technique is a powerful tool providing concrete ways to achieve obfuscation. Recent papers ([22,1]) presented ways to mix such tools with permutation polynomials modulo 2n in order to make them more resilient to SMT solvers. However, because of limitations regarding the inversion of such permutations, the set of permutation polynomials presented suffer some restrictions. Such restrictions bring several methods of arithmetic simplification, decreasing their effectiveness at hiding information. In this work, we present general methods for permutation polynomials inversion. Those methods allow us to remove some of the restrictions presented in the literature, making simplification methods less effective. We discuss complexity and limits of these methods, and conclude that not only current simplification methods may not be as effective as we thought, but they are still many uses of polynomial permutations in obfuscation that are yet to be explored.

The secure two-party computation (S2PC) protocols SHADE and GSHADE have been introduced by Bringer et al. in the last two years. The protocol GSHADE permits to compute different distances (Hamming, Euclidean, Mahalanobis) quite efficiently and is one of the most efficient compared to other S2PC methods. Thus this protocol can be used to efficiently compute one-to-many identification for several biometrics data (iris, face, fingerprint). In this paper, we introduce two extensions of GSHADE. The first one enables us to evaluate new multiplicative functions. This way, we show how to apply GSHADE to a classical machine learning algorithm. The second one is a new proposal to secure GSHADE against malicious adversaries following the recent dual execution and cut-and-choose strategies. The additional cost is very small. By preserving the GSHADE's structure, our extensions are very efficient compared to other S2PC methods.

In this paper we provide a framework to analyze the effect of uniform sampling on graph optimization problems. Interestingly, we apply this framework to a general class of graph optimization problems that we call heavy subgraph problems, and show that uniform sampling preserves a 1-ε approximate solution to these problems. This class contains many interesting problems such as densest subgraph, directed densest subgraph, densest bipartite subgraph, d-max cut, and d-sum-max clustering. As an immediate impact of this result, one can use uniform sampling to solve these problems in streaming, turnstile or Map-Reduce settings. Indeed, our results by characterizing heavy subgraph problems address Open Problem 13 at the IITK Workshop on Algorithms for Data Streams in 2006 regarding the effects of subsampling, in the context of graph streams. Recently Bhattacharya et al. in STOC 2015 provide the first one pass algorithm for the densest subgraph problem in the streaming model with additions and deletions to its edges, i.e., for dynamic graph streams. They present a (0.5-ε)-approximation algorithm using \textasciitildeO(n) space, where factors of ε and log(n) are suppressed in the \textasciitildeO notation. In this paper we improve the (0.5-ε)-approximation algorithm of Bhattacharya et al. by providing a (1-ε)-approximation algorithm using \textasciitildeO(n) space.

In a secret sharing scheme a dealer shares a secret s among n parties such that an adversary corrupting up to t parties does not learn s, while any t+1 parties can efficiently recover s. Over a long period of time all parties may be corrupted thus violating the threshold, which is accounted for in Proactive Secret Sharing (PSS). PSS schemes periodically rerandomize (refresh) the shares of the secret and invalidate old ones. PSS retains confidentiality even when all parties are corrupted over the lifetime of the secret, but no more than t during a certain window of time, called the refresh period. Existing PSS schemes only guarantee secrecy in the presence of an honest majority with less than n2 total corruptions during a refresh period; an adversary corrupting a single additional party, even if only passively, obtains the secret. This work is the first feasibility result demonstrating PSS tolerating a dishonest majority, it introduces the first PSS scheme secure against t passive adversaries without recovery of lost shares, it can also recover from honest faulty parties losing their shares, and when tolerating e faults the scheme tolerates t passive corruptions. A non-robust version of the scheme can tolerate t active adversaries, and mixed adversaries that control a combination of passively and actively corrupted parties that are a majority, but where less than n/2-e of such corruptions are active. We achieve these high thresholds with O(n4) communication when sharing a single secret, and O(n3) communication when sharing multiple secrets in batches.

Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices. It allows a trusted party (verifier) to learn the state of a remote, and potentially malware-infected, device (prover). Most existing approaches are static in nature and only check whether benign software is initially loaded on the prover. However, they are vulnerable to runtime attacks that hijack the application's control or data flow, e.g., via return-oriented programming or data-oriented exploits. As a concrete step towards more comprehensive runtime remote attestation, we present the design and implementation of Control-FLow ATtestation (C-FLAT) that enables remote attestation of an application's control-flow path, without requiring the source code. We describe a full prototype implementation of C-FLAT on Raspberry Pi using its ARM TrustZone hardware security extensions. We evaluate C-FLAT's performance using a real-world embedded (cyber-physical) application, and demonstrate its efficacy against control-flow hijacking attacks.

Security patterns are generic solutions that can be applied since early stages of software life to overcome recurrent security weaknesses. Their generic nature and growing number make their choice difficult, even for experts in system design. To help them on the pattern choice, this paper proposes a semi-automatic methodology of classification and the classification itself, which exposes relationships among software weaknesses, security principles and security patterns. It expresses which patterns remove a given weakness with respect to the security principles that have to be addressed to fix the weakness. The methodology is based on seven steps, which anatomize patterns and weaknesses into set of more precise sub-properties that are associated through a hierarchical organization of security principles. These steps provide the detailed justifications of the resulting classification and allow its upgrade. Without loss of generality, this classification has been established for Web applications and covers 185 software weaknesses, 26 security patterns and 66 security principles. Research supported by the industrial chair on Digital Confidence (http://confiance-numerique.clermont-universite.fr/index-en.html).