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Intro -- Preface -- Organization -- Contents - Part III -- Information Security -- Security Protocol for Cloud Storage Based on Block-Chain -- 1 Introduction -- 2 Related Works -- 3 Design Security Protocol -- 3.1 Basic Theory -- 3.2 Security Protocol -- 3.3 Analysis Models -- 4 Conclusions and Future Work -- References -- A New Pairing-Based Scheme for Anonymous Communication System -- 1 Introduction -- 2 The Weil Pairing -- 2.1 The Properties of Weil Pairing -- 2.2 Some Hard Problems in Elliptic Curve -- 3 Our Scheme -- 3.1 Parameters Setup and Key Extract -- 3.2 Encryption and Decryption -- 3.3 Digital Signature -- 3.4 Key Exchange -- 3.5 Key Revocation -- 4 Analysis of Our Scheme -- 4.1 Comparison Between Our Scheme and IBC -- 4.2 Security Analysis of Our Scheme -- 4.3 Anonymity Analysis of Our Scheme -- 5 Conclusion -- References -- A Two-Way Quantum Key Distribution Based on Two Different States -- 1 Introduction -- 2 The Proposed Protocol -- 3 Security Analyses -- 3.1 Modification Attack -- 3.2 Intercept-and-Resend Attack -- 3.3 Trojan-Horse Attack -- 4 Conclusions -- References -- Fault-Tolerant Semi-quantum Secure Direct Communication Scheme in Noisy Environment -- 1 Introduction -- 2 About Collective Noise -- 3 The Scheme -- 3.1 In the Collective Phase Shift Noise -- 3.2 In the Collective Rotation Noise -- 4 Security Analysis -- 4.1 Intercept - Measure - Refire attacks -- 4.2 Modification Attack -- 5 Conclusion -- References -- Review on Variant Consensus Algorithms Based on PBFT -- 1 Introduction -- 2 The Classical Consensus Algorithm -- 3 Variant Consensus Algorithms Based on PBFT -- 3.1 BBFT and FastBFT -- 3.2 MinBFT -- 3.3 OBFT, CheapBFT and Zyzzyva -- 3.4 DBFT and CDBFT -- 4 The Performance of Variant Consensus Algorithm -- 5 Conclusion -- References -- A New Network Intrusion Detection Method Based on Deep Neural Network.

Comunicació presentada al SACMAT '20: The 25th ACM Symposium on Access Control Models and Technologies, celebrat del 10 al 12 de juny de 2020 a Barcelona, Espanya. ; There has been over the past decade a rapid change towards computational environments that are comprised of large and diverse sets of devices, many of them mobile, which can connect in flexible and context-dependent ways. Examples range from networks where we can have communications between powerful cloud centers, to the myriad of simple sensor devices on the IoT. As the management of these dynamic environments becomes ever more complex, we want to propose policy migrations as a methodology to simplify the management of security policies by re-utilizing and re-deploying existing policies as the systems change. We are interested in understanding the challenges raised answering the following question: given a security policy that is being enforced in a particular source computational device, what does it entail to migrate this policy to be enforced in a different target device? Because of the differences between devices and because these devices cannot be seen in isolation but in the context where they are deployed, the meaning of the policy enforced in the source device needs to be re-interpreted and implemented in the context of the target device. The aim of the paper is to present a formal framework to evaluate the appropriateness of the migration. ; This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Jorge Lobo was also supported by the Spanish Ministry of Economy and Competitiveness under Grant Numbers TIN201681032P, MDM20150502, and the U.S. Army Research Office under agreement number W911NF1910432.

The volume presents, in a synergistic manner, significant theoretical and practical contributions in the area of social media reputation and authorship measurement, visualization, and modeling. The book justifies and proposes contributions to a future agenda for understanding the requirements for making social media authorship more transparent. Building on work presented in a previous volume of this series, Roles, Trust, and Reputation in Social Media Knowledge Markets, this book discusses new tools, applications, services, and algorithms that are needed for authoring content in a real-time publishing world. These insights may help people who interact and create content through social media better assess their potential for knowledge creation. They may also assist in analyzing audience attitudes, perceptions, and behavior in informal social media or in formal organizational structures. In addition, the volume includes several chapters that analyze the higher order ethical, critical thinking, and philosophical principles that may be used to ground social media authorship. Together, the perspectives presented in this volume help us understand how social media content is created and how its impact can be evaluated. The chapters demonstrate thought leadership through new ways of constructing social media experiences and making traces of social interaction visible. Transparency in Social Media aims to help researchers and practitioners design services, tools, or methods of analysis that encourage a more transparent process of interaction and communication on social media. Knowing who has added what content and with what authority to a specific online social media project can help the user community better understand, evaluate and make decisions and, ultimately, act on the basis of such information

Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this article, we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users' interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user's characteristics from two datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and users' group psychology can result in mis-linkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mis-linked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results.

AbstractThis paper describes collaborative commerce (c‐commerce); it essentially combines e‐commerce, knowledge management and collaboration to carry out transactions and other activities within and across organizations. We first discuss the building blocks for c‐commerce. Then we describe models and federated architectures. Next we analyze the strategic role of knowledge management for c‐commerce as well as discussing managerial and business implications. Finally, we provide directions for c‐commerce. Copyright © 2002 John Wiley & Sons, Ltd.

Comunicació presentada a: AAAI Conference on Artificial Intelligence celebrat del 27 de gener a l'1 de febrer de 2019 a Hawaii, Estats Units d'Amèrica. ; In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed. ; This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. It was also partially supported by the Spanish Ministry of Economy and Competitiveness under Grant Numbers TIN-2016-81032-P & MDM-2015-0502.