Biblio

At present, cloud computing technology has made outstanding contributions to the Internet in data unification and sharing applications. However, the problem of information security in cloud computing environment has to be paid attention to and effective measures have to be taken to solve it. In order to control the data security under cloud services, the DS evidence theory method is introduced. The trust management mechanism is established from the source of big data, and a cloud computing security assessment model is constructed to achieve the quantifiable analysis purpose of cloud computing security assessment. Through the simulation, the innovative way of quantifying the confidence criterion through big data trust management and DS evidence theory not only regulates the data credible quantification mechanism under cloud computing, but also improves the effectiveness of cloud computing security assessment, providing a friendly service support platform for subsequent cloud computing service.

It is expected that the concept of Internet of everything will be realized in 2020 because of the coming of the 5G wireless communication technology. Internet of Things (IoT) services in various fields require different types of network service features, such as mobility, security, bandwidth, latency, reliability and control strategies. In order to solve the complex requirements and provide customized services, a new network architecture is needed. To change the traditional control mode used in the traditional network architecture, the Software Defined Network (SDN) is proposed. First, SDN divides the network into the Control Plane and Data Plane and then delegates the network management authority to the controller of the control layer. This allows centralized control of connections of a large number of devices. Second, SDN can help realizing the network slicing in the aspect of network layer. With the network slicing technology proposed by 5G, it can cut the 5G network out of multiple virtual networks and each virtual network is to support the needs of diverse users. In this work, we design and develop a network slicing framework. The contributions of this article are two folds. First, through SDN technology, we develop to provide the corresponding end-to-end (E2E) network slicing for IoT applications with different requirements. Second, we develop a dynamic network slice resource scheduling and management method based on SDN to meet the services' requirements with time-varying characteristics. This is usually observed in streaming and services with bursty traffic. A prototyping system is completed. The effectiveness of the system is demonstrated by using an electronic fence application as a use case.

Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints. SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks. Software Defined Network (SDN) based Network Function Virtualization (NFV) has emerged as a solution that allows dynamic SFC composition and traffic steering in a cloud network. We propose an SDN enabled Universal Policy Checking (SUPC) framework, to provide 1) Flow Composition and Ordering by translating various SF rules into the OpenFlow format. This ensures elimination of redundant rules and policy compliance in SFC. 2) Flow conflict analysis to identify conflicts in header space and actions between various SF rules. Our results show a significant reduction in SF rules on composition. Additionally, our conflict checking mechanism was able to identify several rule conflicts that pose security, efficiency, and service availability issues in the cloud network.

Emerging networking technologies, such as cloud and Software Defined Networking, provide flexibility, elasticity and functionalities to change the network configurations over time. However, changes also impose unpredictable security postures at different times, creating difficulties to the security assessment of the network. To address this issue, we propose a stateless security risk assessment, which combines the security posture of network states at different times to provide an overall security overview. This paper describes the methodologies of the stateless security risk assessment. Our approach is applicable to any emerging networking technologies with dynamic changes.

To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.

In recent trends, Cyber-Physical Systems (CPS) and Internet of Things interpret an evolution of computerized integration connectivity. The specific research challenges in CPS as security, privacy, data analytics, participate sensing, smart decision making. In addition, The challenges in Wireless Sensor Network (WSN) includes secure architecture, energy efficient protocols and quality of services. In this paper, we present an architectures of CPS and its protocols and applications. We propose software related mobile sensing paradigm namely Mobile Sensor Information Agent (MSIA). It works as plug-in based for CPS middleware and scalable applications in mobile devices. The working principle MSIA is acts intermediary device and gathers data from a various external sensors and its upload to cloud on demand. CPS needs tight integration between cyber world and man-made physical world to achieve stability, security, reliability, robustness, and efficiency in the system. Emerging software-defined networking (SDN) can be integrated as the communication infrastructure with CPS infrastructure to accomplish such system. Thus we propose a possible SDN-based CPS framework to improve the performance of the system.

Information communicated within cyber-physical systems (CPSs) is often used in determining the physical states of such systems, and malicious adversaries may intercept these communications in order to infer future states of a CPS or its components. Accordingly, there arises a need to protect the state values of a system. Recently, the notion of differential privacy has been used to protect state trajectories in dynamical systems, and it is this notion of privacy that we use here to protect the state trajectories of CPSs. We incorporate a cloud computer to coordinate the agents comprising the CPSs of interest, and the cloud offers the ability to remotely coordinate many agents, rapidly perform computations, and broadcast the results, making it a natural fit for systems with many interacting agents or components. Striving for broad applicability, we solve infinite-horizon linear-quadratic-regulator (LQR) problems, and each agent protects its own state trajectory by adding noise to its states before they are sent to the cloud. The cloud then uses these state values to generate optimal inputs for the agents. As a result, private data are fed into feedback loops at each iteration, and each noisy term affects every future state of every agent. In this paper, we show that the differentially private LQR problem can be related to the well-studied linear-quadratic-Gaussian (LQG) problem, and we provide bounds on how agents' privacy requirements affect the cloud's ability to generate optimal feedback control values for the agents. These results are illustrated in numerical simulations.

This work studies the optimization of spectrum pooling for the downlink of a multi-tenant Cloud Radio Access Network (C-RAN) system in the presence of inter-tenant privacy constraints. The spectrum available for downlink transmission is partitioned into private and shared subbands, and the participating operators cooperate to serve the user equipments (UEs) on the shared subband. The network of each operator consists of a cloud processor (CP) that is connected to proprietary radio units (RUs) by means of finite-capacity fronthaul links. In order to enable inter-operator cooperation, the CPs of the participating operators are also connected by finite-capacity backhaul links. Inter-operator cooperation may hence result in loss of privacy. The problem of optimizing the bandwidth allocation, precoding, and fronthaul/backhaul compression strategies is tackled under constraints on backhaul and fronthaul capacity, as well as on per-RU transmit power and inter-onerator privacy.

With cloud computing's development, more users are decide to store information on the cloud server. Owing to the cloud server's insecurity, many documents should be encrypted to avoid information leakage before being sent to the cloud. Nevertheless, it leads to the problem that plaintext search techniques can not be directly applied to the ciphertext search. In this case, many searchable encryption schemes based on single data owner model have been proposed. But, the actual situation is that users want to do research with encrypted documents originating from various data owners. This paper puts forward a privacy-preserving scheme that is based on fuzzy multi-keyword search (PPFMKS) for multiple data owners. For the sake of espousing fuzzy multi-keyword and accurate search, secure indexes on the basis of Locality-Sensitive Hashing (LSH) and Bloom Filter (BF)are established. To guarantee the search privacy under multiple data owners model, a new encryption method allowing that different data owners have diverse keys to encrypt files is proposed. This method also solves the high cost caused by inconvenience of key management.

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol, which we implemented and evaluated based on the real-world scenario of product cost optimisation. It is based on homomorphic encryption and enables cloud-based KPI comparison, providing a variety of statistical measures. The theoretical and empirical evaluation of our benchmarking system underlines its practicability.

With the challenge of the vast amount of data generated by devices at the edge of networks, new architecture needs a well-established data service model that accounts for privacy concerns. This paper presents an architecture of data transmission and a data portfolio with privacy for fog-to-cloud (DPPforF2C). We would like to propose a practical data model with privacy from a digitalized information perspective at fog nodes. In addition, we also propose an architecture for implicating the privacy of DPPforF2C used in fog computing. Technically, we design a data portfolio based on the Message Queuing Telemetry Transport (MQTT) and the Advanced Message Queuing Protocol (AMQP). We aim to propose sample data models with privacy architecture because there are some differences in the data obtained from IoT devices and sensors. Thus, we propose an architecture with the privacy of DPPforF2C for publishing data from edge devices to fog and to cloud servers that could be applied to fog architecture in the future.

Healthcare is traditionally a data-intensive domain, where physicians needs complete and updated anamnesis of their patients to take the best medical decisions. Dematerialization of the medical documents and the consequent health information systems to share electronic health records among healthcare providers are paving the way to an effective solution to this issue. However, they are also paving the way of non-negligible privacy issues that are limiting the full application of these technologies. Encryption is a valuable means to resolve such issues, however the current schemes are not able to cope with all the needs and challenges that the cloud-based sharing of electronic health records imposes. In this work we have investigated the use of a novel scheme where encryption is combined with biometric authentication, and defines a preliminary solution.

One critical challenge of today's cloud services is how to provide an effective search service while preserving user privacy. In this paper, we propose a wildcard-based multi-keyword fuzzy search (WMFS) scheme over the encrypted data, which tolerates keyword misspellings by exploiting the indecomposable property of primes. Compared with existing secure fuzzy search schemes, our WMFS scheme has the following merits: 1) Efficiency. It eliminates the requirement of a predefined dictionary and thus supports updates efficiently. 2) High accuracy. It eliminates the false positive and false negative introduced by specific data structures and thus allows the user to retrieve files as accurate as possible. 3) Flexibility. It gives the user great flexibility to specify different search patterns including keyword and substring matching. Extensive experiments on a real data set demonstrate the effectiveness and efficiency of our scheme.

We consider a cloud-based control architecture for a linear plant with Gaussian process noise, where the state of the plant contains a client's sensitive information. We assume that the cloud tries to estimate the state while executing a designated control algorithm. The mutual information between the client's actual state and the cloud's estimate is adopted as a measure of privacy loss. We discuss the necessity of uplink and downlink privacy masks. After observing that privacy is not necessarily a monotone function of the noise levels of privacy masks, we discuss the joint design procedure for uplink and downlink privacy masks. Finally, the trade-off between privacy and control performance is explored.

Control algorithms, like model predictive control, can be computationally expensive and may benefit from being executed over the cloud. This is especially the case for nodes at the edge of a network since they tend to have reduced computational capabilities. However, control over the cloud requires transmission of sensitive data (e.g., system dynamics, measurements) which undermines privacy of these nodes. When choosing a method to protect the privacy of these data, efficiency must be considered to the same extent as privacy guarantees to ensure adequate control performance. In this paper, we review a transformation-based method for protecting privacy, previously introduced by the authors, and quantify the level of privacy it provides. Moreover, we also consider the case of adversaries with side knowledge and quantify how much privacy is lost as a function of the side knowledge of the adversary.

With the popularity of smart devices and the widespread use of the Wi-Fi-based indoor localization, edge computing is becoming the mainstream paradigm of processing massive sensing data to acquire indoor localization service. However, these data which were conveyed to train the localization model unintentionally contain some sensitive information of users/devices, and were released without any protection may cause serious privacy leakage. To solve this issue, we propose a lightweight differential privacy-preserving mechanism for the edge computing environment. We extend ε-differential privacy theory to a mature machine learning localization technology to achieve privacy protection while training the localization model. Experimental results on multiple real-world datasets show that, compared with the original localization technology without privacy-preserving, our proposed scheme can achieve high accuracy of indoor localization while providing differential privacy guarantee. Through regulating the value of ε, the data quality loss of our method can be controlled up to 8.9% and the time consumption can be almost negligible. Therefore, our scheme can be efficiently applied in the edge networks and provides some guidance on indoor localization privacy protection in the edge computing.

More and more security and privacy issues are arising as new technologies, such as big data and cloud computing, are widely applied in nowadays. For decreasing the privacy breaches in access control system under opening and cross-domain environment. In this paper, we suggest a game and risk based access model for privacy preserving by employing Shannon information and game theory. After defining the notions of Privacy Risk and Privacy Violation Access, a high-level framework of game theoretical risk based access control is proposed. Further, we present formulas for estimating the risk value of access request and user, construct and analyze the game model of the proposed access control by using a multi-stage two player game. There exists sub-game perfect Nash equilibrium each stage in the risk based access control and it's suitable to protect the privacy by limiting the privacy violation access requests.

Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing models.

The burgeoning Internet of Things (IoT) has offered unprecedented opportunities for innovations and applications that are continuously changing our life. At the same time, the large amount of pervasive IoT applications have posed paramount threats to the user's security and privacy. While a lot of efforts have been dedicated to deal with such threats from the hardware, the software, and the applications, in this paper, we argue and envision that more effective and comprehensive protection for IoT systems can only be achieved via a cross-layer approach. As such, we present our initial design of XLF, a cross-layer framework towards this goal. XLF can secure the IoT systems not only from each individual layer of device, network, and service, but also through the information aggregation and correlation of different layers.

This paper provides hardware-independent authentication named as Intelligent Authentication Scheme, which rectifies the design weaknesses that may be exploited by various security attacks. The Intelligent Authentication Scheme protects against various types of security attacks such as password-guessing attack, replay attack, streaming bots attack (denial of service), keylogger, screenlogger and phishing attack. Besides reducing the overall cost, it also balances both security and usability. It is a unique authentication scheme.

Software defined systems use virtualization technologies to provide an abstraction of the hardware infrastructure at different layers. Ultimately, the adoption of software defined systems in all cloud infrastructure components will lead to Software Defined Cloud Computing. Nevertheless, virtualization has already been used for years and is a key element of cloud computing. Traditionally, virtual machines are deployed in cloud infrastructure and used to execute applications on common operating systems. New lightweight virtualization technologies, such as containers and unikernels, appeared later to improve resource efficiency and facilitate the decomposition of big monolithic applications into multiple, smaller services. In this work, we present and empirically evaluate four popular unikernel technologies, Docker containers and Docker LinuxKit. We deployed containers both on bare metal and on virtual machines. To fairly evaluate their performance, we created similar applications for unikernels and containers. Additionally, we deployed full-fledged database applications ported on both virtualization technologies. Although in bibliography there are a few studies which compare unikernels and containers, in our study for the first time, we provide a comprehensive performance evaluation of clean-slate and legacy unikernels, Docker containers and Docker LinuxKit.

With the gradual progress of cloud computing, big data, network virtualization and other network technology. The traditional network architecture can no longer support this huge business. At this time, the clean slate team defined a new network architecture, SDN (Software Defined Network). It has brought about tremendous changes in the development of today's networks. The controller sends the flow table down to the switch, and the data flow is forwarded through matching flow table items. However, the current flow table resources of the SDN switch are very limited. Therefore, this paper studies the technology of the latest SDN Flow table optimization at home and abroad, proposes an efficient optimization scheme of Flow table item on the betweenness centrality through the main road selection algorithm, and realizes related applications by setting up experimental topology. Experiments show that this scheme can greatly reduce the number of flow table items of switches, especially the more hosts there are in the topology, the more obvious the experimental effect is. And the experiment proves that the optimization success rate is over 80%.

The notion of attribute-based encryption with outsourced decryption (OD-ABE) was proposed by Green, Hohenberger, and Waters. In OD-ABE, the ABE ciphertext is converted to a partially-decrypted ciphertext that has a shorter bit length and a faster decryption time than that of the ABE ciphertext. In particular, the transformation can be performed by a powerful third party with a public transformation key. In this paper, we propose a generic approach for constructing ABE with outsourced decryption from standard ABE, as long as the later satisfies some additional properties. Its security can be reduced to the underlying standard ABE in the selective security model by a black-box way. To avoid the drawback of selective security in practice, we further propose a modified decryption outsourcing mode so that our generic construction can be adapted to satisfying adaptive security. This partially solves the open problem of constructing an OD-ABE scheme, and its adaptive security can be reduced to the underlying ABE scheme in a black-box way. Then, we present some concrete constructions that not only encompass existing ABE outsourcing schemes of Green et al., but also result in new selectively/adaptively-secure OD-ABE schemes with more efficient transformation key generation algorithm. Finally, we use the PBC library to test the efficiency of our schemes and compare the results with some previous ones, which shows that our schemes are more efficient in terms of decryption outsourcing and transformation key generation.

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.