Efficient incremental authentication for the updated data in fog computing

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

Cloud computing performs several functionalities, and one of the most important functionalities is the storage and processing of data or information. With day-by-day enhancement of technology, cloud has been overburdened, and to address this issue, the concept of fog computing has been introduced. Fog computing is an extension of the properties of cloud computing to the network’s edge and additionally overcomes its limitations. Despite the growing fame of fog services, assuring the security and privacy of data is still a big challenge. Distributed denial of service (DDoS) attack is a well-known threat among the security concerns and an important research challenge when talking particularly about security of data in fog computing environment. Therefore, this chapter presents a survey which encompasses the various concepts of fog computing, DDoS attacks and some DDoS mitigation techniques, thus providing a comprehensive review. In addition, it beholds the future work in this domain. This chapter will attract new researchers and also strengthen the concept of fog computing.

A secure cross-domain authentication scheme with perfect forward security and complete anonymity in fog computing

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

One of the main lines of research in functional encryption (FE) has consisted in studying the security notions for FE and their achievability. This study was initiated by [Boneh et al. – TCC’11, O’Neill – ePrint’10] where it was first shown that for FE the indistinguishability-based (IND) security notion is not sufficient in the sense that there are FE schemes that are provably IND-Secure but concretely insecure. For this reason, researchers investigated the achievability of Simulation-based (SIM) security, a stronger notion of security. Unfortunately, the above-mentioned works and others [e.g., Agrawal et al. – CRYPTO’13] have shown strong impossibility results for SIM-Security. One way to overcome these impossibility results was first suggested in the work of Boneh et al. where it was shown how to construct, in the Random Oracle (RO) model, SIM-Secure FE for restricted functionalities and was asked the generalization to more complex functionalities as a challenging problem in the area. Subsequently, [De Caro et al. – CRYPTO’13] proposed a candidate construction of SIM-Secure FE for all circuits in the RO model assuming the existence of an IND-Secure FE scheme for circuits with RO gates. To our knowledge there are no proposed candidate IND-Secure FE schemes for circuits with RO gates and they seem unlikely to exist. We propose the first constructions of SIM-Secure FE schemes in the RO model that overcome the current impossibility results in different settings. We can do that because we resort to the two following models: Both results also assume the RO model, but not functionalities with RO gates and rely on extractability obfuscation [Boyle et al. – TCC’14] (and other standard primitives) secure only in the standard model.

A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

Internet of Things (IoT) has been changing the way of day-to-day life activities because of the use of sensors, wearables, and smartphones. The connected devices form the basis for smart systems such as smartwatches, smart cities, smart cars, smart grids, and smart buildings. The devices are capable of collecting large quantities of data. The data once collected need to be analyzed intelligently to leverage the potential of the data. The existing analytical approach of carrying out analytics lies in the principles of data transfer to cloud for storage and processing, subsequently delivering the results to the IoT-based software applications. Fog Computing (FC) has paved the way for providing an alternate way for IoT data analytics compared to the centralized cloud computing approach for analytics. FC is a paradigm based on the approach of carrying out computation and analytics at the edge devices rather than the cloud. However, the latency analysis in FC remains a challenge. In this chapter, the data reduction techniques are explored with FC for IoT applications. The techniques included are Missing Values Ratio, Low Variance Filter, High Correlation Filter, Principal Component Analysis (PCA), Random Forrest/Ensemble Trees, Backward Feature Elimination, and Forward Feature Construction. A case study with PCA method for Fog Data Analytics is discussed at the end of the chapter. It demonstrates the effectiveness of data reduction methods in FC.

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift partial computational and storage overhead from end users to chosen fog nodes. Furthermore, the basic LFGS system is improved to support conjunctive keyword search and attribute update to avoid returning irrelevant search results and illegal accesses. The formal security analysis shows that the LFGS system can resist Chosen-Keyword Attack (CKA) and Chosen-Plaintext Attack (CPA), and the simulation using a real-world dataset demonstrates that the LFGS system is efficient and feasible in practice.

In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks (VANETs). By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme (CLASC) is proposed by using a fog computing framework that supports mobility, low latency, and location awareness. It is combined with online/offline encryption (OOE) technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and nondeniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie-Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. The simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system but also achieve higher efficiency in computing and communication.

The certificateless online/offline aggregate signcryption scheme combines the characteristics of the certificateless aggregate signcryption scheme and the online/offline encryption scheme, which can increase efficiency while simultaneously reducing consumption. Some schemes can meet the requirements of confidentiality and real-time transmission of the data in ad hoc networks (VANETS). However, they are unable to withstand collusion attempts. A brand-new certificateless aggregate signcryption approach is suggested to overcome this problem. First, combining fog computing with online/offline encryption (OOE) technology can increase efficiency while simultaneously reducing consumption. Second, we may achieve effective information authentication and vehicle identification using aggregation and vehicle pseudonym systems. Third, the anti-collusion component is suggested as a viable defense against collusion assaults since certain methods are unable to withstand such attacks. Additionally, it is demonstrated that the technique has unforgeability and secrecy, and can fend off collusion attacks using the random oracle model. The findings demonstrate that our system can not only ensure the confidentiality and the real-time transmission of data but also resist collusion attacks without raising computational costs.

The distinguishing feature of the Fog Computing (FC) paradigm is that FC spreads communication and computing resources over the wireless access network, so as to provide resource augmentation to resource and energy-limited wireless (possibly mobile) devices. Since FC would lead to substantial reductions in energy consumption and access latency, it will play a key role in the realization of the Fog of Everything (FoE) paradigm. The core challenge of the resulting FoE paradigm is tomaterialize the seamless convergence of three distinct disciplines, namely, broadband mobile communication, cloud computing, and Internet of Everything (IoE). In this paper, we present a new IoE architecture for FC in order to implement the resulting FoE technological platform. Then, we elaborate the related Quality of Service (QoS) requirements to be satisfied by the underlying FoE technological platform. Furthermore, in order to corroborate the conclusion that advancements in the envisioned architecture description, we present: (i) the proposed energy-aware algorithm adopt Fog data center; and, (ii) the obtained numerical performance, for a real-world case study that shows that our approach saves energy consumption impressively in theFog data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm.

Certificate-based cryptography solves certificate revocation problem and eliminate third-party queries in the traditional PKI and avoid key escrow in the ID-based cryptography. It is a better alternative for the traditional public key system. In this paper, we proposed an efficient certificate based signature scheme based on Schnorr signature and zero-knowledge proof technique. Our proposed scheme doesn't use pairing operator which is the most expensive computation. Then we show that our proposed scheme is proven to be against two forgeable attacks in the random oracles model, and the security is related to that of Schnorr signature.

An authenticated encryption scheme is a message transmission scheme, which can send a message in a secure and authentic way, and allows the specified recipient to simultaneously recover and verify the validity of a message. In the large message transmission, traditional authenticated encryption schemes have the disadvantage that communication overheads and the computation costs are too high. In this work, we propose a secure authentication encryption scheme and show that the scheme is secure in the random oracle model. Comparisons of our proposed scheme with Wu-Hsus scheme and Huang-Changs scheme, the proposed scheme is more efficient for computation complexity and communication cost. Finally, we extend our proposed scheme to adapt to the authenticated encryption for a large message, which is secure against the message block being reordered, replicated or partially deleted during transmission.

Heterogeneous online/offline signcryption for secure communication in Internet of Things