OpenRaiser/Intern-Atlas huggingface dataset:
Privacy Preserving Enforcement of Sensitive Policies in Outsourced and Distributed Environments
Venue: arXiv cs.CR 2013
Year: 2013

The enforcement of sensitive policies in untrusted environments is still an open challenge for policy-based systems. On the one hand, taking any appropriate security decision requires access to these policies. On the other hand, if such access is allowed in an untrusted environment then confidential information might be leaked by the policies. The key challenge is how to enforce sensitive policies and protect content in untrusted environments. In the context of untrusted environments, we mainly distinguish between outsourced and distributed environments. The most attractive paradigms concerning outsourced and distributed environments are cloud computing and opportunistic networks, respectively. In this dissertation, we present the design, technical and implementation details of our proposed policy-based access control mechanisms for untrusted environments. First of all, we provide full confidentiality of access policies in outsourced environments, where service providers do not learn private information about policies. We support expressive policies and take into account contextual information. The system entities do not share any encryption keys. For complex user management, we offer the full-fledged Role-Based Access Control (RBAC) policies. In opportunistic networks, we protect content by specifying expressive policies. In our proposed approach, brokers match subscriptions against policies associated with content without compromising privacy of subscribers. As a result, unauthorised brokers neither gain access to content nor learn policies and authorised nodes gain access only if they satisfy policies specified by publishers. Our proposed system provides scalable key management in which loosely-coupled publishers and subscribers communicate without any prior contact. Finally, we have developed a prototype of the system that runs on real smartphones and analysed its performance.
Opacity with Orwellian Observers and Intransitive Non-interference
Venue: arXiv cs.CR 2013
Year: 2013

Opacity is a general behavioural security scheme flexible enough to account for several specific properties. Some secret set of behaviors of a system is opaque if a passive attacker can never tell whether the observed behavior is a secret one or not. Instead of considering the case of static observability where the set of observable events is fixed off line or dynamic observability where the set of observable events changes over time depending on the history of the trace, we consider Orwellian partial observability where unobservable events are not revealed unless a downgrading event occurs in the future of the trace. We show how to verify that some regular secret is opaque for a regular language L w.r.t. an Orwellian projection while it has been proved undecidable even for a regular language L w.r.t. a general Orwellian observation function. We finally illustrate relevancy of our results by proving the equivalence between the opacity property of regular secrets w.r.t. Orwellian projection and the intransitive non-interference property.
The Cloud's Cloudy Moment: A Systematic Survey of Public Cloud Service Outage
Venue: arXiv cs.DC 2013
Year: 2013

Inadequate service availability is the top concern when employing Cloud computing. It has been recognized that zero downtime is impossible for large-scale Internet services. By learning from the previous and others' mistakes, nevertheless, it is possible for Cloud vendors to minimize the risk of future downtime or at least keep the downtime short. To facilitate summarizing lessons for Cloud providers, we performed a systematic survey of public Cloud service outage events. This paper reports the result of this survey. In addition to a set of findings, our work generated a lessons framework by classifying the outage root causes. The framework can in turn be used to arrange outage lessons for reference by Cloud providers. By including potentially new root causes, this lessons framework will be smoothly expanded in our future work.
Early Observations on Performance of Google Compute Engine for Scientific Computing
Venue: arXiv cs.DC 2013
Year: 2013

Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCE's scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazon's EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.
A proposal of a faster variant of known provably secure PRBGs
Venue: arXiv cs.CR 2013
Year: 2013

We make a new proposal about how to use in an effective way a CSPRBG (Computationally Secure Pseudo Random Bit Generator) for cryptographic purposes. We introduce the definitions of TCSPRBG (Typical CSPRBG) and SCSPRBG (Special CSPRBG). In particular the definition of SCSPRBG synthetizes in a simple way our proposal of how to modify a CSPRBG in order to achieve a higher throughput rate, while retaining some essential features of its computational security. We then summarize which should be, in our opinion, a "standard way" to use a CSPRBG for cryptographic purposes. We eventually present as an application, a particular SCSPRBG for which we can achieve throughput rates greater than $100$ Mbits/sec on current mobile devices.
Trackability with Imprecise Localization
Venue: arXiv cs.RO 2013
Year: 2013

Imagine a tracking agent $P$ who wants to follow a moving target $Q$ in $d$-dimensional Euclidean space. The tracker has access to a noisy location sensor that reports an estimate $\tilde{Q}(t)$ of the target's true location $Q(t)$ at time $t$, where $||Q(T) - \tilde{Q}(T)||$ represents the sensor's localization error. We study the limits of tracking performance under this kind of sensing imprecision. In particular, we investigate (1) what is $P$'s best strategy to follow $Q$ if both $P$ and $Q$ can move with equal speed, (2) at what rate does the distance $||Q(t) - P(t)||$ grow under worst-case localization noise, (3) if $P$ wants to keep $Q$ within a prescribed distance $L$, how much faster does it need to move, and (4) what is the effect of obstacles on the tracking performance, etc. Under a relative error model of noise, we are able to give upper and lower bounds for the worst-case tracking performance, both with or without obstacles.
Automated Coin Recognition System using ANN
Venue: arXiv cs.CV 2013
Year: 2013

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination Rs. 1, 2, 5 and 10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.
Invariant Factorization Of Time-Series
Venue: arXiv cs.LG 2013
Year: 2013

Time-series classification is an important domain of machine learning and a plethora of methods have been developed for the task. In comparison to existing approaches, this study presents a novel method which decomposes a time-series dataset into latent patterns and membership weights of local segments to those patterns. The process is formalized as a constrained objective function and a tailored stochastic coordinate descent optimization is applied. The time-series are projected to a new feature representation consisting of the sums of the membership weights, which captures frequencies of local patterns. Features from various sliding window sizes are concatenated in order to encapsulate the interaction of patterns from different sizes. Finally, a large-scale experimental comparison against 6 state of the art baselines and 43 real life datasets is conducted. The proposed method outperforms all the baselines with statistically significant margins in terms of prediction accuracy.
Non-associative key establishment protocols and their implementation
Venue: arXiv cs.CR 2013
Year: 2013

We provide implementation details for non-associative key establishment protocols. In particular, we describe the implementation of non-associative key establishment protocols for all left self-distributive and all mutually left distributive systems.
A Coverage Strategy for Wireless Sensor Networks in a Three-dimensional Environment
Venue: arXiv cs.NI 2013
Year: 2013

Coverage is one of the fundamental issues in wireless sensor networks (WSNs). It reflects the ability of WSNs to detect the fields of interest. In a real sensor networks application, the detection area is always non-ideal and the terrain of the detection area is often more complex in applications of three-dimensional sensor networks. Consequently, many of the existing coverage strategies cannot be directly applied to three-dimensional spaces. This paper presents a new coverage strategy for the three-dimensional sensor networks. Sensor nodes are uniformly distributed. The cost factor is utilized to construct the perceived probability and the classical watershed algorithm after the transformation of points from the three-dimensional space to the two-dimensional plane using the dimensionality reduction method, which can maintain the topology characteristic of the non-linear terrain. The detection probability in the optimal breath path is used as the measure to evaluate the coverage. Simulation results indicate that the proposed strategy can determine the coverage with fewer nodes, while achieving the coverage requirements of the networks.
A Search Strategy of Level-Based Flooding for the Internet of Things
Venue: arXiv cs.NI 2013
Year: 2013

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales.
Dihedral angles and orthogonal polyhedra
Venue: arXiv cs.CG 2013
Year: 2013

Consider an orthogonal polyhedron, i.e., a polyhedron where (at least after a suitable rotation) all faces are perpendicular to a coordinate axis, and hence all edges are parallel to a coordinate axis. Clearly, any facial angle and any dihedral angle is a multiple of $π/2$. In this note we explore the converse: if the facial and/or dihedral angles are all multiples of $π/2$, is the polyhedron necessarily orthogonal? The case of facial angles was answered previously. In this note we show that if both the facial and dihedral angles are multiples of $π/2$ then the polyhedron is orthogonal (presuming connectivity), and we give examples to show that the condition for dihedral angles alone does not suffice.
Evaluating IEEE 802.15.4 for Cyber-Physical Systems
Venue: arXiv cs.NI 2013
Year: 2013

With rapid advancements in sensing, networking, and computing technologies, recent years have witnessed the emergence of cyber-physical systems (CPS) in a broad range of application domains. CPS is a new class of engineered systems that features the integration of computation, communications, and control. In contrast to general-purpose computing systems, many cyber-physical applications are safety-cricial. These applications impose considerable requirements on quality of service (QoS) of the employed networking infrastruture. Since IEEE 802.15.4 has been widely considered as a suitable protocol for CPS over wireless sensor and actuator networks, it is of vital importance to evaluate its performance extensively. Serving for this purpose, this paper will analyze the performance of IEEE 802.15.4 standard operating in different modes respectively. Extensive simulations have been conducted to examine how network QoS will be impacted by some critical parameters. The results are presented and analyzed, which provide some useful insights for network parameter configuration and optimization for CPS design.
Greedy Column Subset Selection for Large-scale Data Sets
Venue: arXiv cs.DS 2013
Year: 2013

In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively-distributed data, which is formally known as the Column Subset Selection (CSS) problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a low-dimensional embedding of the data points or computing a low-rank approximation of the corresponding matrix. This paper presents a fast and accurate greedy algorithm for large-scale column subset selection. The algorithm minimizes an objective function which measures the reconstruction error of the data matrix based on the subset of selected columns. The paper first presents a centralized greedy algorithm for column subset selection which depends on a novel recursive formula for calculating the reconstruction error of the data matrix. The paper then presents a MapReduce algorithm which selects a few representative columns from a matrix whose columns are massively distributed across several commodity machines. The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized. The paper demonstrates the effectiveness and efficiency of the proposed algorithm through an empirical evaluation on benchmark data sets.
Data Offloading in Load Coupled Networks: A Utility Maximization Framework
Venue: arXiv cs.IT 2013
Year: 2013

We provide a general framework for the problem of data offloading in a heterogeneous wireless network, where some demand of cellular users is served by a complementary network. The complementary network is either a small-cell network that shares the same resources as the cellular network, or a WiFi network that uses orthogonal resources. For a given demand served in a cellular network, the load, or the level of resource usage, of each cell depends in a non-linear manner on the load of other cells due to the mutual coupling of interference seen by one another. With load coupling, we optimize the demand to be served in the cellular or the complementary networks, so as to maximize a utility function. We consider three representative utility functions that balance, to varying degrees, the revenue from serving the users vs the user fairness. We establish conditions for which the optimization problem has a feasible solution and is convex, and hence tractable to numerical computations. Finally, we propose a strategy with theoretical justification to constrain the load to some maximum value, as required for practical implementation. Numerical studies are conducted for both under-loaded and over-loaded networks.
A New Approach to Constraint Weight Learning for Variable Ordering in CSPs
Venue: arXiv cs.AI 2013
Year: 2013

A Constraint Satisfaction Problem (CSP) is a framework used for modeling and solving constrained problems. Tree-search algorithms like backtracking try to construct a solution to a CSP by selecting the variables of the problem one after another. The order in which these algorithm select the variables potentially have significant impact on the search performance. Various heuristics have been proposed for choosing good variable ordering. Many powerful variable ordering heuristics weigh the constraints first and then utilize the weights for selecting good order of the variables. Constraint weighting are basically employed to identify global bottlenecks in a CSP. In this paper, we propose a new approach for learning weights for the constraints using competitive coevolutionary Genetic Algorithm (GA). Weights learned by the coevolutionary GA later help to make better choices for the first few variables in a search. In the competitive coevolutionary GA, constraints and candidate solutions for a CSP evolve together through an inverse fitness interaction process. We have conducted experiments on several random, quasi-random and patterned instances to measure the efficiency of the proposed approach. The results and analysis show that the proposed approach is good at learning weights to distinguish the hard constraints for quasi-random instances and forced satisfiable random instances generated with the Model RB. For other type of instances, RNDI still seems to be the best approach as our experiments show.
Theoretical Bitcoin Attacks with less than Half of the Computational Power (draft)
Venue: arXiv cs.CR 2013
Year: 2013

A widespread security claim of the Bitcoin system, presented in the original Bitcoin white-paper, states that the security of the system is guaranteed as long as there is no attacker in possession of half or more of the total computational power used to maintain the system. This claim, however, is proved based on theoretically flawed assumptions. In the paper we analyze two kinds of attacks based on two theoretical flaws: the Block Discarding Attack and the Difficulty Raising Attack. We argue that the current theoretical limit of attacker's fraction of total computational power essential for the security of the system is in a sense not $\frac{1}{2}$ but a bit less than $\frac{1}{4}$, and outline proposals for protocol change that can raise this limit to be as close to $\frac{1}{2}$ as we want. The basic idea of the Block Discarding Attack has been noted as early as 2010, and lately was independently though-of and analyzed by both author of this paper and authors of a most recently pre-print published paper. We thus focus on the major differences of our analysis, and try to explain the unfortunate surprising coincidence. To the best of our knowledge, the second attack is presented here for the first time.
Approximating Quadratic 0-1 Programming via SOCP
Venue: arXiv cs.CC 2013
Year: 2013

We consider the problem of approximating Quadratic O-1 Integer Programs with bounded number of constraints and non-negative constraint matrix entries, which we term as PIQP. We describe and analyze a randomized algorithm based on a program with hyperbolic constraints (a Second-Order Cone Programming -SOCP- formulation) that achieves an approximation ratio of $O(a_{max} \frac{n}{β(n)})$, where $a_{max}$ is the maximum size of an entry in the constraint matrix and $β(n) \leq \min_i{W_i} $, where $W_i$ are the constant terms that define the constraint inequalities. We note that by appropriately choosing $β(n)$ the randomized algorithm, when combined with other algorithms that achieve good approximations for smaller values of $ W_i$, allows better algorithms for the complete range of $W_i$. This, together with a greedy algorithm, provides a $O^*(a_{max} n^{1/2} )$ factor approximation, where $O^*$ hides logarithmic terms. Our solution is achieved by a randomization of the optimal solution to the relaxed version of the hyperbolic program. We show that this solution provides the approximation bounds using concentration bounds provided by Chernoff-Hoeffding and Kim-Vu.
Development of Display Ads Retrieval System to Match Publisher's Contents
Venue: arXiv cs.CY 2013
Year: 2013

The technological transformation and automation of digital content delivery has revolutionized the media industry. Advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising. In this paper, the types of internet advertising to be discussed on are contextual and sponsored search ads. These types of advertising have the central challenge of finding the best match between a given context and a suitable advertisement, through a principled method. Furthermore, there are four main players that exist in the Internet advertising ecosystem: users, advertisers, ad exchange and publishers. Hence, to find ways to counter the central challenge, the paper addresses two objectives: how to successfully make the best contextual ads selections to match to a web page content to ensure that there is a valuable connection between the web page and the contextual ads. All methods, discussions, conclusion and future recommendations are presented as per sections. Hence, in order to prove the working mechanism of matching contextual ads and web pages, web pages together with the ads matching system are developed as a prototype.
Link Quality and MAC-Overhead aware Predictive Preemptive Routing Protocol for Mobile Ad hoc Network
Venue: arXiv cs.NI 2013
Year: 2013

In Ad Hoc networks, route failure may occur due to less received power, mobility, congestion and node failures. Many approaches have been proposed in literature to solve this problem, where a node predicts pre-emptively the route failure that occurs with the less received power. However, this approach encounters some difficulties, especially in scenario without mobility where route failures may arise. In this paper, we propose an improvement of AODV protocol called LO-PPAODV (Link Quality and MAC-Overhead aware Predictive Preemptive AODV). This protocol is based on new metric combine more routing metrics (Link Quality, MAC Overhead) between each node and one hop neighbor. Also we propose a cross-layer networking mechanism to distinguish between both situations, failures due to congestion or mobility, and consequently avoiding unnecessary route repair process. The LO-PPAODV was implemented using NS-2. The simulation results show that our approach improves the overall performance of the network. It reduces the average end to end delay, the routing overhead, MAC errors and route errors, and increases the packet delivery fraction of the network.