Abstract A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.
Smart cyber-physical systems (SCPS) have become an important part of the IT landscape. Often these systems include IoT devices that allow effective and easy acquisition of data in areas such as healthcare, smart cities, smart vehicles, and smart homes @cite_66 . Data mining and analysis are among the primary goals of collecting data from SCPS. The infrastructural extensions of SCPSs have contributed to the exponential growth in the number of IoT sensors, but security is often overlooked, and the devices become a source of privacy leak. The security and privacy concerns of big data and data streams are not entirely new, but require constant attention due to technological advancements of the environments and the devices used @cite_40 . Confidentiality, authentication, and authorization are just a few of the concerns @cite_44 @cite_83 @cite_32 . Many studies have raised the importance of privacy and security of SCPS due to their heavy use of personally identifiable information (PII) @cite_84 . Controlling access via authentication @cite_67 , attribute-based encryption @cite_74 , temporal and location-based access control @cite_67 and employing constraint-based protocols @cite_61 are some examples of improving privacy of SCPS.
@cite_61 @cite_67 @cite_32 @cite_84 @cite_44 @cite_40 @cite_83 @cite_74 @cite_66
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Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Smart Cyber--Physical Systems (sCPS) are modern CPS systems that are engineered to seamlessly integrate a large number of computation and physical components; they need to control entities in their environment in a smart and collective way to achieve a high degree of effectiveness and efficiency. At the same time, these systems are supposed to be safe and secure, deal with environment dynamicity and uncertainty, cope with external threats, and optimize their behavior to achieve the best possible outcome. This "smartness" typically stems from highly cooperative behavior, self--awareness, self--adaptation, and selfoptimization. Most of the "smartness" is implemented in software, which makes the software one of the most complex and most critical constituents of sCPS. As the specifics of sCPS render traditional software engineering approaches not directly applicable, new and innovative approaches to software engineering of sCPS need to be sought. This paper reports on the results of the Second International Workshop on Software Engineering for Smart Cyber--Physical Systems (SEsCPS 2016), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS. The IoT is pervading our daily activities and lives with devices scattered all over our cities, transport systems, buildings, homes and bodies. This invasion of devices with sensors and communication capabilities brings big concerns, mainly about the privacy and confidentiality of the collected information. These concerns hinder the wide adoption of the IoT. To overcome them, in this work, we present an Blockchain-based architecture for IoT access authorizations. Following the IoT tendency requirements, our architecture is user transparent, user friendly, fully decentralized, scalable, fault tolerant and compatible with a wide range of today's access control models used in the IoT. Finally, our architecture also has a secure way to establish relationships between users, devices and group of both, allowing the assignment of attributes for these relationships and their use in the access control authorization. The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large scale IoT based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain enabled decentralized capability based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identity based capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine grained AC solution to IoT systems. Internet of Things (IoT) is characterized by heterogeneous technologies, which concur to the provisioning of innovative services in various application domains. In this scenario, the satisfaction of security and privacy requirements plays a fundamental role. Such requirements include data confidentiality and authentication, access control within the IoT network, privacy and trust among users and things, and the enforcement of security and privacy policies. Traditional security countermeasures cannot be directly applied to IoT technologies due to the different standards and communication stacks involved. Moreover, the high number of interconnected devices arises scalability issues; therefore a flexible infrastructure is needed able to deal with security threats in such a dynamic environment. In this survey we present the main research challenges and the existing solutions in the field of IoT security, identifying open issues, and suggesting some hints for future research. Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known as “miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant relative to its security and privacy gains. With the advancement of Internet of Things (IoT) technology and rapid growth of WSN applications, provides an opportunity to connect WSN to IoT, which results in the secure sensor data can be accessible via in secure Internet. The integration of WSN and IoT effects lots of security challenges and requires strict user authentication mechanism. Quite a few isolated user verification or authentication schemes using the password, the biometrics and the smart card have been proposed in the literature. In 2013, A.K designed a biometric-based remote user verification scheme using smart card for heterogeneous wireless sensor networks. A.K insisted that their scheme is secure against several known cryptographic attacks. Unfortunately, in this manuscript we will show that their scheme fails to resist replay attack, user impersonation attack, failure to accomplish mutual authentication and failure to provide data privacy. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Despite technological advances, most smart objects in the Internet of Things (IoT) cannot be accessed using technologies designed and developed for interacting with powerful Internet servers. IoT use cases involve devices that not only have limited resources, but also they are not always connected to the Internet and are physically exposed to tampering. In this paper, we describe the design, development, and evaluation of a smart contract-based solution that allows end-users to securely interact with smart devices. Our approach enables access control, Thing authentication, and payments in a fully decentralized setting, taking at the same time into consideration the limitations and constraints imposed by both blockchain technologies and the IoT paradigm. Our prototype implementation is based on existing technologies, i.e., Ethereum smart contracts, which makes it realistic and fundamentally secure. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Smart home IoT devices are becoming increasingly popular. Modern programmable smart home hubs such as SmartThings enable homeowners to manage devices in sophisticated ways to save energy, improve security, and provide conveniences. Unfortunately, many smart home systems contain vulnerabilities, potentially impacting home security and privacy. This paper presents Vigilia, a system that shrinks the attack surface of smart home IoT systems by restricting the network access of devices. As existing smart home systems are closed, we have created an open implementation of a similar programming and configuration model in Vigilia and extended the execution environment to maximally restrict communications by instantiating device-based network permissions. We have implemented and compared Vigilia with forefront IoT-defense systems; our results demonstrate that Vigilia outperforms these systems and incurs negligible overhead. The term "Internet of Things (IoT)" expresses a huge network of smart and connected objects which can interact with other devices without our interposition. Radio frequency identification (RFID) is a great technology and an interesting candidate to provide communications for IoT networks, but numerous security and privacy issues need to be considered. In this paper, we analyze the security and the privacy of a new RFID authentication protocol proposed by in 2014. We prove that although have tried to present a secure and untraceable authentication protocol, their protocol still suffers from several security and privacy weaknesses which make it vulnerable to various security and privacy attacks. We present our privacy analysis based on a well-known formal privacy model which is presented by Ouafi and Phan in 2008. Moreover, to stop such attacks on the protocol and increase the performance of ’s scheme, we present some modifications and propound an improved version of the protocol. Finally, the security and the privacy of the proposed protocol were analyzed against various attacks. KEWORDS Internet of things; RFID authentication protocols; Security and privacy; Ouafi-Phan privacy model; EPC C1 G2 standard. * Corresponding author email: mj.emadi@aut.ac.ir The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols. Internet of Things (IoT), also referred to as the Internet of Objects, is envisioned as a transformative approach for providing numerous services. Compact smart devices constitute an essential part of IoT. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT. Moreover, commercialization of IoT has led to public security concerns, including personal privacy issues, threat of cyber attacks, and organized crime. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this survey attempts to provide a comprehensive list of vulnerabilities and countermeasures against them on the edge-side layer of IoT, which consists of three levels: (i) edge nodes, (ii) communication, and (iii) edge computing. To achieve this goal, we first briefly describe three widely-known IoT reference models and define security in the context of IoT. Second, we discuss the possible applications of IoT and potential motivations of the attackers who target this new paradigm. Third, we discuss different attacks and threats. Fourth, we describe possible countermeasures against these attacks. Finally, we introduce two emerging security challenges not yet explained in detail in previous literature. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Smart devices equipped with powerful sensing, computing and networking capabilities have proliferated lately, ranging from popular smartphones and tablets to Internet appliances, smart TVs, and others that will soon appear (e.g., watches, glasses, and clothes). One key feature of such devices is their ability to incorporate third-party apps from a variety of markets. This poses strong security and privacy issues to users and infrastructure operators, particularly through software of malicious (or dubious) nature that can easily get access to the services provided by the device and collect sensory data and personal information. Malware in current smart devices —mostly smartphones and tablets— have rocketed in the last few years, in some cases supported by sophisticated techniques purposely designed to overcome security architectures currently in use by such devices. Even though important advances have been made on malware detection in traditional personal computers during the last decades, adopting and adapting those techniques to smart devices is a challenging problem. For example, power consumption is one major constraint that makes unaffordable to run traditional detection engines on the device, while externalized (i.e., cloud-based) techniques rise many privacy concerns. This article examines the problem of malware in smart devices and recent progress made in detection techniques. We first present a detailed analysis on how malware has evolved over the last years for the most popular platforms. We identify exhibited behaviors, pursued goals, infection and distribution strategies, etc. and provide numerous examples through case studies of the most relevant specimens. We next survey, classify and discuss efforts made on detecting both malware and other suspicious software (grayware), concentrating on the 20 most relevant techniques proposed between 2010 and 2013. Based on the conclusions extracted from this study, we finally provide constructive discussion on open research problems and areas where we believe that more work is needed. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ...