The existence of errors or inconsistencies in the configuration of security components, such as filtering routers and or firewalls, may lead to weak access control policies -- potentially easy to be evaded by unauthorized parties. We present in this paper a proposal to create, manage, and deploy consistent policies in those components in an efficient way. To do so, we combine two main approaches. The first approach is the use of an aggregation mechanism that yields consistent configurations or signals inconsistencies. Through this mechanism we can fold existing policies of a given system and create a consistent and global set of access control rules -- easy to maintain and manage by using a single syntax. The second approach is the use of a refinement mechanism that guarantees the proper deployment of such a global set of rules into the system, yet free of inconsistencies.
However, their work does not fix, from our point of view, clear semantics, and their concept of role becomes ambiguous as we pointed out in @cite_10 . Another work based on policy refinement is the RBNS model @cite_16 . However, and although the authors claim that their work is based on the RBAC model @cite_13 , it seems that they only keep from this model only the concept of role. Indeed, the specification of network entities and role and permission assignments are not rigorous and does not fit any reality @cite_10 .
@cite_13 @cite_16 @cite_10
2144565093 2115403742 1986989788 1967221886 2141696417 1981058821 2076773434 178254799 2466714650 2798791651 2250726251 2110111529 2460423734 2971432649 2094858374 145176944 2149557440 2950290711 2020634547 2125890412
Several models for incorporating spatial constraints into role-based access control (RBAC) have been proposed, and researchers are now focusing on the challenge of ensuring such policies are enforced correctly. However, existing approaches have a major shortcoming, as they assume the server is trustworthy and require complete disclosure of sensitive location information by the user. In this work, we propose a novel framework and a set of protocols to solve this problem. Specifically, in our scheme, a user provides a service provider with role and location tokens along with a request. The service provider consults with a role authority and a location authority to verify the tokens and evaluate the policy. However, none of the servers learn the requesting user's identity, role, or location. In this paper, we define the protocols and the policy enforcement scheme, and present a formal proof of a number of security properties. In this paper we present a context-aware RBAC (CARBAC) model for pervasive computing applications. The design of this model has been guided by the context-based access control requirements of such applications. These requirements are related to users' memberships in roles, permission executions by role members, and context-based dynamic integration of services in the environment with an application. Context information is used in role admission policies, in policies related to permission executions by role members, and in policies related to accessing of dynamically interfaced services by role members. The dynamic nature of context information requires model-level support for revocations of role memberships and permission activations when certain context conditions fail to hold. Based on this model we present a programming framework for building context-aware applications, providing mechanisms for specifying and enforcing context-based access control requirements. Role-Based Access Control (RBAC) is supported directly or in a closely related form, by a number of products. This article presents a formalization of RBAC using graph transformations that is a graphical specification technique based on a generalization of classical string grammars to nonlinear structures. The proposed formalization provides an intuitive description for the manipulation of graph structures as they occur in information systems access control and a precise specification of static and dynamic consistency conditions on graphs and graph transformations. The formalism captures the RBAC models published in the literature, and also allows a uniform treatment of user roles and administrative roles, and a detailed analysis of the decentralization of administrative roles. In this article, we present a comprehensive approach for privacy preserving access control based on the notion of purpose. In our model, purpose information associated with a given data element specifies the intended use of the data element. A key feature of our model is that it allows multiple purposes to be associated with each data element and also supports explicit prohibitions, thus allowing privacy officers to specify that some data should not be used for certain purposes. An important issue addressed in this article is the granularity of data labeling, i.e., the units of data with which purposes can be associated. We address this issue in the context of relational databases and propose four different labeling schemes, each providing a different granularity. We also propose an approach to represent purpose information, which results in low storage overhead, and we exploit query modification techniques to support access control based on purpose information. Another contribution of our work is that we address the problem of how to determine the purpose for which certain data are accessed by a given user. Our proposed solution relies on role-based access control (RBAC) models as well as the notion of conditional role which is based on the notions of role attribute and system attribute. Role-based access control (RBAC) models have generated a great interest in the security community as a powerful and generalized approach to security management. In many practical scenarios, users may be restricted to assume roles only at predefined time periods. Furthermore, roles may only be invoked on prespecified intervals of time depending upon when certain actions are permitted. To capture such dynamic aspects of a role, a temporal RBAC (TRBAC) model has been recently proposed. However, the TRBAC model addresses the role enabling constraints only. In This work, we propose a generalized temporal role-based access control (GTRBAC) model capable of expressing a wider range of temporal constraints. In particular, the model allows expressing periodic as well as duration constraints on roles, user-role assignments, and role-permission assignments. In an interval, activation of a role can further be restricted as a result of numerous activation constraints including cardinality constraints and maximum active duration constraints. The GTRBAC model extends the syntactic structure of the TRBAC model and its event and trigger expressions subsume those of TRBAC. Furthermore, GTRBAC allows expressing role hierarchies and separation of duty (SoD) constraints for specifying fine-grained temporal semantics. The Relationship-Based Access Control (ReBAC) model was recently proposed as a general-purpose access control model. It supports the natural expression of parameterized roles, the composition of policies, and the delegation of trust. Fong proposed a policy language that is based on Modal Logic for expressing and composing ReBAC policies. A natural question is whether such a language is representationally complete, that is, whether the language is capable of expressing all ReBAC policies that one is interested in expressing. In this work, we argue that the extensive use of what we call Relational Policies is what distinguishes ReBAC from traditional access control models. We show that Fong's policy language is representationally incomplete in that certain previously studied Relational Policies are not expressible in the language. We introduce two extensions to the policy language of Fong, and prove that the extended policy language is representationally complete with respect to a well-defined subclass of Relational Policies. We consider spatial stochastic models of downlink heterogeneous cellular networks (HCNs) with multiple tiers, where the base stations (BSs) of each tier have a particular spatial density, transmission power and path-loss exponent. Prior works on such spatial models of HCNs assume, due to its tractability, that the BSs are deployed according to homogeneous Poisson point processes. This means that the BSs are located independently of each other and their spatial correlation is ignored. In the current paper, we propose two spatial models for the analysis of downlink HCNs, in which the BSs are deployed according to @a-Ginibre point processes. The @a-Ginibre point processes constitute a class of determinantal point processes and account for the repulsion between the BSs. Besides, the degree of repulsion is adjustable according to the value of @[email protected]?(0,1]. In one proposed model, the BSs of different tiers are deployed according to mutually independent @a-Ginibre processes, where the @a can take different values for the different tiers. In the other model, all the BSs are deployed according to an @a-Ginibre point process and they are classified into multiple tiers by mutually independent marks. For these proposed models, we derive computable representations for the coverage probability of a typical user-the probability that the downlink signal-to-interference-plus-noise ratio for the typical user achieves a target threshold. We exhibit the results of some numerical experiments and compare the proposed models and the Poisson based model. Dans cette these, nous avons etudie les problemes de decisions sequentielles, avec comme application la gestion de stocks d'energie. Traditionnellement, ces problemes sont resolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexite du probleme, amenent a faire des simplifications sur le modele pour pouvoir faire fonctionner ces methodes.Nous avons donc etudie une methode alternative, qui ne requiert pas de simplifications du modele: Monte Carlo Tree Search (MCTS). Nous avons commence par etendre le MCTS classique (qui s’applique aux domaines finis et deterministes) aux domaines continus et stochastiques. Pour cela, nous avons utilise la methode de Double Progressive Widening (DPW), qui permet de gerer le ratio entre largeur et profondeur de l’arbre, a l’aide de deux meta parametres. Nous avons aussi propose une heuristique nommee Blind Value (BV) pour ameliorer la recherche de nouvelles actions, en utilisant l’information donnee par les simulations passees. D’autre part, nous avons etendu l’heuristique RAVE aux domaines continus. Enfin, nous avons propose deux nouvelles methodes pour faire remonter l’information dans l’arbre, qui ont beaucoup ameliore la vitesse de convergence sur deux cas tests.Une part importante de notre travail a ete de proposer une facon de meler MCTS avec des heuristiques rapides pre-existantes. C’est une idee particulierement interessante dans le cas de la gestion d’energie, car ces problemes sont pour le moment resolus de maniere approchee. Nous avons montre comment utiliser Direct Policy Search (DPS) pour rechercher une politique par defaut efficace, qui est ensuite utilisee a l’interieur de MCTS. Les resultats experimentaux sont tres encourageants.Nous avons aussi applique MCTS a des processus markoviens partiellement observables (POMDP), avec comme exemple le jeu de demineur. Dans ce cas, les algorithmes actuels ne sont pas optimaux, et notre approche l’est, en transformant le POMDP en MDP, par un changement de vecteur d’etat.Enfin, nous avons utilise MCTS dans un cadre de meta-bandit, pour resoudre des problemes d’investissement. Le choix d’investissement est fait par des algorithmes de bandits a bras multiples, tandis que l’evaluation de chaque bras est faite par MCTS.Une des conclusions importantes de ces travaux est que MCTS en continu a besoin de tres peu d’hypotheses (uniquement un modele generatif du probleme), converge vers l’optimum, et peut facilement ameliorer des methodes suboptimales existantes. Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, entities, and entity-types; (2) we use neural attention modeling to incorporate multiple paths; (3) we learn to share strength in a single RNN that represents logical composition across all relations. On a largescale Freebase+ClueWeb prediction task, we achieve 25 error reduction, and a 53 error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84 versus previous state-of-the-art. The code and data are available at this https URL Effective convolutional features play an important role in saliency estimation but how to learn powerful features for saliency is still a challenging task. FCN-based methods directly apply multi-level convolutional features without distinction, which leads to sub-optimal results due to the distraction from redundant details. In this paper, we propose a novel attention guided network which selectively integrates multi-level contextual information in a progressive manner. Attentive features generated by our network can alleviate distraction of background thus achieve better performance. On the other hand, it is observed that most of existing algorithms conduct salient object detection by exploiting side-output features of the backbone feature extraction network. However, shallower layers of backbone network lack the ability to obtain global semantic information, which limits the effective feature learning. To address the problem, we introduce multi-path recurrent feedback to enhance our proposed progressive attention driven framework. Through multi-path recurrent connections, global semantic information from the top convolutional layer is transferred to shallower layers, which intrinsically refines the entire network. Experimental results on six benchmark datasets demonstrate that our algorithm performs favorably against the state-of-the-art approaches. We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task. We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset. Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS's) and Bayesian networks (BNs). FRS's provide an excellent representation for the organizational structure of large complex domains, but their applicability is limited because of their inability to deal with uncertainty and noise. BNs provide an intuitive and coherent probabilistic representation of our uncertainty, but are very limited in their ability to handle complex structured domains. In this paper, we provide a language that cleanly integrates these approaches, preserving the advantages of both. Our approach allows us to provide natural and compact definitions of probability models for a class, in a way that is local to the class frame. These models can be instantiated for any set of interconnected instances, resulting in a coherent probability distribution over the instance properties. Our language also allows us to represent important types of uncertainty that cannot be accomodated within the framework of traditional BNs: uncertainty over the set of entities present in our model, and uncertainty about the relationships between these entities. We provide an inference algorithm for our language via a reduction to inference in standard Bayesian networks. We describe an implemented system that allows most of the main frame systems in existence today to annotate their knowledge bases with probabilistic information, and to use that information in answering probabilistic queries. This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively. Abstract In this paper, we present a unifying analysis for redundancy systems with cancel-on-start ( c . o . s . ) and cancel-on-complete ( c . o . c . ) with exponentially distributed service requirements. With c . o . s . ( c . o . c . ) all redundant copies are removed as soon as one of the copies starts (completes) service. As a consequence, c . o . s . does not waste any computing resources, as opposed to c . o . c . We show that the c . o . s . model is equivalent to a queueing system with multi-type jobs and servers, which was analyzed in , (2012), and show that c . o . c . (under the assumption of i.i.d. copies) can be analyzed by a generalization of , (2012) where state-dependent departure rates are permitted. This allows us to show that the stationary distribution for both the c . o . c . and c . o . s . models has a product form. We give a detailed first-time analysis for c . o . s and derive a closed form expression for important metrics like mean number of jobs in the system, and probability of waiting. We also note that the c . o . s . model is equivalent to Join-Shortest-Work queue with power of d (JSW( d )). In the latter, an incoming job is dispatched to the server with smallest workload among d randomly chosen ones. Thus, all our results apply mutatis-mutandis to JSW( d ). Comparing the performance of c . o . s . with that of c . o . c . with i.i.d. copies gives the unexpected conclusion (since c . o . s . does not waste any resources) that c . o . s . is worse in terms of mean number of jobs. As part of ancillary results, we illustrate that this is primarily due to the assumption of i.i.d. copies in case of c . o . c . (together with exponentially distributed requirements) and that such assumptions might lead to conclusions that are qualitatively different from that observed in practice. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . We consider what constitutes identities in cryptography. Typical examples include your name and your social-security number, or your fingerprint iris-scan, or your address, or your (non-revoked) public-key coming from some trusted public-key infrastructure. In many situations, however, where you are defines your identity. For example, we know the role of a bank-teller behind a bullet-proof bank window not because she shows us her credentials but by merely knowing her location. In this paper, we initiate the study of cryptographic protocols where the identity (or other credentials and inputs) of a party are derived from its geographic location. We start by considering the central task in this setting, i.e., securely verifying the position of a device. Despite much work in this area, we show that in the Vanilla (or standard) model, the above task (i.e., of secure positioning) is impossible to achieve. In light of the above impossibility result, we then turn to the Bounded Storage Model and formalize and construct information theoretically secure protocols for two fundamental tasks: Secure Positioning; and Position Based Key Exchange. We then show that these tasks are in fact universal in this setting --- we show how we can use them to realize Secure Multi-Party Computation.Our main contribution in this paper is threefold: to place the problem of secure positioning on a sound theoretical footing; to prove a strong impossibility result that simultaneously shows the insecurity of previous attempts at the problem; and to present positive results by showing that the bounded-storage framework is, in fact, one of the "right" frameworks (there may be others) to study the foundations of position-based cryptography. Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DT-RNN model which uses dependency trees to embed sentences into a vector space in order to retrieve images that are described by those sentences. Unlike previous RNN-based models which use constituency trees, DT-RNNs naturally focus on the action and agents in a sentence. They are better able to abstract from the details of word order and syntactic expression. DT-RNNs outperform other recursive and recurrent neural networks, kernelized CCA and a bag-of-words baseline on the tasks of finding an image that fits a sentence description and vice versa. They also give more similar representations to sentences that describe the same image. We present novel methods for analyzing the activation patterns of RNNs from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings trained on predicting the representations of the visual scene corresponding to an input sentence, and predicting the next word in the same sentence. Based on our proposed method to estimate the amount of contribution of individual tokens in the input to the final prediction of the networks we show that the image prediction pathway: a) is sensitive to the information structure of the sentence b) pays selective attention to lexical categories and grammatical functions that carry semantic information c) learns to treat the same input token differently depending on its grammatical functions in the sentence. In contrast the language model is comparatively more sensitive to words with a syntactic function. Furthermore, we propose methods to ex- plore the function of individual hidden units in RNNs and show that the two pathways of the architecture in our case study contain specialized units tuned to patterns informative for the task, some of which can carry activations to later time steps to encode long-term dependencies. ACM Sigcomm 2006 published a paper [26] which was perceived to unify the deterministic and stochastic branches of the network calculus (abbreviated throughout as DNC and SNC) [39]. Unfortunately, this seemingly fundamental unification---which has raised the hope of a straightforward transfer of all results from DNC to SNC---is invalid. To substantiate this claim, we demonstrate that for the class of stationary and ergodic processes, which is prevalent in traffic modelling, the probabilistic arrival model from [26] is quasi-deterministic, i.e., the underlying probabilities are either zero or one. Thus, the probabilistic framework from [26] is unable to account for statistical multiplexing gain, which is in fact the raison d'etre of packet-switched networks. Other previous formulations of SNC can capture statistical multiplexing gain, yet require additional assumptions [12], [22] or are more involved [14], [9] [28], and do not allow for a straightforward transfer of results from DNC. So, in essence, there is no free lunch in this endeavor. Our intention in this paper is to go beyond presenting a negative result by providing a comprehensive perspective on network calculus. To that end, we attempt to illustrate the fundamental concepts and features of network calculus in a systematic way, and also to rigorously clarify some key facts as well as misconceptions. We touch in particular on the relationship between linear systems, classical queueing theory, and network calculus, and on the lingering issue of tightness of network calculus bounds. We give a rigorous result illustrating that the statistical multiplexing gain scales as Ω(√N), as long as some small violations of system performance constraints are tolerable. This demonstrates that the network calculus can capture actual system behavior tightly when applied carefully. Thus, we positively conclude that it still holds promise as a valuable systematic methodology for the performance analysis of computer and communication systems, though the unification of DNC and SNC remains an open, yet quite elusive task. This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized @math -learning with compact state representation algorithm, which is named @math -learning. Moreover, a decentralized version of the @math -learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized @math -learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction.