Applications of blockchain technology in privacy preserving and data security for real time (data) applications

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Internet of Things (IoT) is one of the most powerful platforms that incorporates several other technological components within itself. The IoT ecosystem comprises devices, communications, protocols, analytics, cloud, automation, etc. Its magnitude keeps on increasing with the addition of tools and services. While IoT has many advantages like connectivity, efficiency, and convenience, it is a known fact that privacy and security issues are prevalent in the IoT network. To minimize the security and privacy issues, we propose a blockchain-based solution. In this paper, we design policies based on smart contacts, which is a self-enforcing agreement embedded in computer code managed by a blockchain. We propose three different policies: Hardware and Device Security Policies, Access and Authentication policies, and Application security for the IoT network. Since blockchain-based solutions ensure trust and stability, this may be one of the most robust techniques to alleviate the IoT network’s security and privacy issues. Also, we calculate the throughput and latency of the IoT enabled blockchain network and compare the power consumption of the IoT device at the time of data request with other proposed systems.

Nowadays, smart applications are becoming part of small-scale to large-scale industries. Smart applications are making farming, tourism and hospitality, energy sector healthcare, supply chain, and so on smarter than ever. Industrial Internet of Things (IIoT), which is also called Industry 4.0, is the new evolution in the industrial sector that includes automation and smart applications. Internet of Things (IoT) is one of the major pillars of Industry 4.0. IoT devices that collect real-time data from the environment and exchange data among themselves. However, the security and privacy of IoT devices are a major concern as data transmission happens over public networks. To handle the issues, blockchain technology (BCT) can be one of the solutions. The blockchain proved its potential in enhancing the security and privacy of Internet data with various applications. In this regard, the chapter provides an overview of the idea of incorporating blockchain in IoT to overcome the security issues of IoT systems. The main objective of this chapter is to how blockchain features overcome the security and privacy threat in IoT system. Initially, the chapter focuses on several security and privacy concerns in IoT. We associate the features of the BCT with the challenges faced in IoT with respect to security and privacy issues. Some of the applications of blockchain in IoT have also been discussed that primarily target security and privacy issues of IoT. The chapter also focuses on challenges and future works in blockchain integrated with IoT infrastructure.

The Internet of Things (IoT) in health care is an emerging trend which provides a substantial amount of efficient and effective services for patients as well as healthcare professionals for the treatment of various diseases. Although the IoT paradigm offers numerous benefits, there are also inherent security and privacy challenges in relation to patient data. This chapter aims to investigate such security and privacy-related issues of the IoT healthcare applications that often operate on Cloud and Fog Computing architectures. In this chapter, 30 peer-reviewed publications from the years 2016 to 2018 are reviewed to construct a structured guide for the identification of related problems. Healthcare software applications, service architectures, patient data, and diverse sensory devices used are some of the factors considered for the collection of data, determination of related security and privacy issues, and implications of the said problems. Based on the factors, a comparison table is developed for analysis. Results show that most of these applications are used for remote patient care and monitoring. Furthermore, the results show that most threats occur due to unauthorized access to data, data breaches, and impersonation. Considering the IoT healthcare applications architecture (consisting of application, communication, device, network, and transport layers), we find that the device layer is the most affected layer regarding the security issues. Since most of the similar studies are limited to providing solutions regarding a specific aspect of security and privacy without considering the entire process, we have carried out additional research for a holistic approach to solve the bigger problem. We suggest that application developers should pay particular attention to these threats when designing IoT healthcare applications to provide secure and efficient applications to enhance the services within the healthcare sector. This study could be helpful to plan mitigation methodologies to conquer the inherent vulnerabilities in the IoT healthcare applications.

Internet of Things (IoT) is an interconnected wireless network where smart nodes (IoT devices) interact with each other in order to exchange data through the communicating medium. IoT technology has become important for people to build smart systems upon the use of technology. Internet of things is realized by the idea of free flow of information among various low-power embedded devices that use the Internet to communicate with one another. In the recent past IoT have grown rapidly and have become an extension of existing universal Internet. It can be easily anticipated that large scale systems equipped with numerous sensors will prevail in our society. With the rise of the Internet of Things (IoT) technology, the number of IoT devices/sensors has also increased significantly Billions of smart devices connected to IoT environment can communicate among themselves using sensors and actuators. This rapid growth and inclusion of IoT technologies in our daily life are facing challenges since most of the devices specially sensors in IoT network are resource constraint in terms of energy, computation capability, etc. Data collected from these sensors sent through the middleware like gateway, routers, etc. to the cloud servers or toward various analytical engine for meaningful knowledge discovery. These processed data and knowledge have lately attracted huge attention, and organizations are excited about the business value of the data that will be generated by deploying such networks. With this advent of IoT it has also attracted various security and privacy concerns. Due to the structurally open IoT architecture and the tremendous usage of the paradigm itself, causes to generate many unconventional security issues for the existing networking technologies. Moreover, since sensor nodes are cooperative within the IoT network, this sharing of data can create new challenges that can disrupt the systems’ regular functionalities and operations. In another aspect the growth of IoT technologies has enhanced by assimilating them with cloud computing and the era IoT-cloud has emerged. With these, some new class of security and privacy issues have also introduced. Furthermore, the commercialization of the IoT has led to several public security concerns including threats of cyber-attacks, privacy issues, and fraud crimes. This chapter gathers the needed information to give a complete picture of security issues and problems faced in IoT communication. In this chapter, we detail the major security as well as privacy issues more specifically. An extensive description of security threats and challenges across the different layers of the architecture of IoT systems is represented. The issues related to IoT-cloud is also highlighted. The light will be shed on the state-of-the-art solutions to the emerging and latest security issues in this field. We hereby present the evolving resolve policies as mined from the research work of various authors in this field that will expose the several research areas in IoT-cloud era.KeywordsInternet of ThingsIoT securityPrivacyCloud server

Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive—new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this article, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.

SummaryInternet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to remotely control these devices. The IoT is pervasive and is almost an integral part of our daily life. These connected devices often obtain user's personal data and store it online. The security of collected data is a big concern in recent times. As devices are becoming increasingly connected, privacy and security issues become more and more critical and these need to be addressed on an urgent basis. IoT implementations and devices are eminently prone to threats that could compromise the security and privacy of the consumers, which, in turn, could influence its practical deployment. In recent past, some research has been carried out to secure IoT devices with an intention to alleviate the security concerns of users. There have been research on blockchain technologies to tackle the privacy and security issues of the collected data in IoT. The purpose of this paper is to highlight the security and privacy issues in IoT systems. To this effect, the paper examines the security issues at each layer in the IoT protocol stack, identifies the under‐lying challenges and key security requirements and provides a brief overview of existing security solutions to safeguard the IoT from the layered context.

Fog nodes are implemented near to end‐users Internet of Things (IoT) devices, which mitigate the impact of low latency, location awareness, and geographic distribution unsupported features of many IoT applications. Moreover, Fog computing decreases the data offload into the Cloud, which decreases the response time. Despite these benefits, Fog computing faces many challenges in meeting security and privacy requirements. These challenges occur due to the limitations of Fog computing resources. In fact, Fog computing may add new security and privacy issues. Although many papers have discussed the Fog security and privacy issues recently, most of these papers have discussed these issues at a very high level. This paper provides a comprehensive understanding of Fog privacy and security issue. In this survey, we review the literature on Fog computing to draw the state‐of‐the‐art of the security and privacy issues raised by Fog computing. The findings of this survey reveal that studying Fog computing is still in its infant stage. Many questions are yet to be answered to address the privacy and security challenges of Fog computing.

The Internet of Things and blockchain technology will be the focus of this research. This study looks at issues of security and privacy. The main challenges it faces, as well as the possibility of using it to improve bitcoin transactions for businesses, are the focus points. The blockchain concept is a distributed ledger with decentralization and data that is dispersed evenly across all nodes in the chain. In terms of data security, the blockchain ensures all three components of the CIA trinity by maintaining data confidentiality, integrity, and availability. Initially, the significance of this technique was limited to crypto currency because it is a one-way function that prevents any type of record fabrication. This concept is currently widely used in medical and public health data security, and many financial institutions, such as Citi Bank, are exploring employing block chain technology to create a breakthrough in the financial realm. This research is a comprehensive review of the literature on the significance of blockchain technology, as well as the security and privacy issues that this technology now faces.

Chapter 4 - Survey on security and privacy issues in IoT healthcare

The Internet of Things (IoT) is a revolutionary element in the sector of healthcare; it assists in bringing efficiency, effectiveness, and accessibility; hence, it is a productive asset to keep in use and also boosts positive outcomes for every patient. The Internet of Things (IoT) in healthcare is the chassis of all connected devices and systems that enables the real-time collection, transmission, and analysis of health data. This chapter describes the fundamental elements of IoT in healthcare, such as wearable devices, sensors, connectivity technologies, data analytics, and mobile applications. It mentions major usages such as RPM (Remote Patient Monitoring), Smart hospitals, personalised medicine, medication management, and telemedicine. It also covers how IoT-enabled diagnostics and treatment connect by looking at real-time data integrated with Electronic Health Records (EHR) and AI decision-making tools. The advantages of IoT in improving patient safety, reducing expenses, and automating healthcare functions are highlighted. It also tackles challenges such as data security and privacy issues, and it discusses interoperability and technical problems like the reliability of networking. This chapter aims to provide a comprehensive overview of how IoT is revolutionising healthcare by improving patient monitoring, enhancing diagnostic accuracy, and enabling personalised, efficient treatments.

Trust and reputation are important terms whether the communication is Humans-to-Human (H2H), Human-Machine-Interaction (HMI) or Machine-to-Machine (M2M). As Cloud computing and the internet of things (IoT) bring new innovations, they also cause various security and privacy issues. As numerous devices are continuously integrating as a core part of IoT, it is necessarily important to consider various security issues such as the trustworthiness of a user or detection of a malicious user. Moreover, fog computing also known as edge computing is revolutionizing the Cloud-based IoT by providing the Cloud services at the edge of the network, which can provide aid in overcoming security, privacy and trust issues. In this work, we propose a context-aware trust evaluation model to evaluate the trustworthiness of a user in a Fog based IoT (FIoT). The proposed approach uses a context-aware multi-source trust and reputation based evaluation system which helps in evaluating the trustworthiness of a user effectively. Further, we use context-aware feedback and feedback crawler system which helps in making trust evaluation unbiased, effective and reliable. Furthermore, we introduce monitor mode for malicious/untrustworthy users, which helps in monitoring the behavior and trustworthiness of a user. The proposed approach uses several tunable factors, which can be tuned based on the system's requirements. The simulations and results indicate that our approach is effective and reliable to evaluate the trustworthiness of a user.

The first Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010) was organized on September 24, 2010 at Barcelona, Spain, in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Privacy and security-related aspects of data mining and machine learning have been the topic of active research during the last decade due to the existence of numerous applications with privacy and/or security requirements. Privacy issues have become a serious concern due to the increasing collection and sharing of personal d ata for purposes like data publishing or data mining. This has led to the development of privacy-preserving data mining and machine learning methods. More general security considerations arise in applications such as biometric authentication, intrusion detection and malware classification. This has led to the development of adversarial learning algorithms, while parallel work in multi-agent settings and in low regret learning algorithms has revealed interesting interplays between learning and game theory. Although significant research has so far been conducted, we are still far from addressing the numerous theoretical and practical challenges that arise in this domain. Firstly, several emerging research areas in data analysis, decision making and machine learning, require new theoretical and applied techniques for the offering of privacy or security. Secondly, there is an urgent need for learning and mining methods with privacy and security guarantees. Thirdly, there is an emerging demand for security applications such as biometric authentication and malware detection. In all cases, the strong interconnections between data mining and machine learning, cryptography and game theory, create the need for the development of multidisciplinary approaches on adversarial learning and mining problems. The aim of the PSDML workshop was to bring together scientists and practitioners who conduct research on privacy and security issues in data mining and machine learning to discuss the most recent advances in these research areas, identify open problem domains and propose possible solutions. The workshop was organized along four core subjects: (a) data privacy and security issues, (b) theoretical aspects of machine learning for security applications, (c) privacy-preserving data mining, machine learning and applications, and (d) security applications of machine learning. The present special issue contains three extended papers that have been selected among the papers presented at PSDML 2010 focusing mainly on the privacy aspects of data mining and machine learning. The first paper by Manas A. Pathak and Bhiksha Raj is titled 'Efficient Protocols for Principal Eigenvector Computation over Private Data'. It presents an efficient secure multiparty computation protocol for computing the principal eigenvector of a collection of data matrices that belong to semi-honest parties, coordinated by a semi-honest arbitrator. The protocol is augmented with randomization, data padding and oblivious transfer to conceal the information which the parties can learn from the intermediate results. The authors provide an analysis of correctness, security and efficiency of the protocol together with experimental results from a prototype implementation. The second paper by Henrik Grosskreutz, Benedikt Lemmen and Stefan Ruping is titled 'Secure Distributed Subgroup Discovery in Horizontally Partitioned Data'. The paper studies the problem of subgroup discovery and presents new secure protocols which support distributed subgroup discovery on horizontally partitioned data. The authors analyze the properties of their top-1 subgroup discovery protocol and prove that it leaks only little information, namely the size of the database and the share of positive records. They also report experimental results which demonstrate the feasibility of the approach by using a prototype implementation of the protocol. The third paper by Gerald Gavin, Julien Velcin and Philippe Aubertin is titled 'Privacy Preserving Aggregation of Secret Classifiers'. This work considers a number of parties, each having its own dataset, who build a private classifier for predicting a binary class variable. The authors develop protocols which allow combining these private classifiers in a privacy-preserving way in order to improve individual predictions. The aggregation is achieved through a secure computation of linear combinations (weighed votes) over the private classifiers. The proposed protocols are shown to be correct and private against any active polynomial adversary. We believe that the selected papers are a good representative of the research in privacy aspects of data mining and machine learning. We hope that you will enjoy reading them.

For quicker reaction times, the data-driven infrastructure known as the Internet of Things (IoT) heavily relies on intelligent sensing devices. IoT devices are now susceptible to more expansive risk surfaces due to the changing cyber threats landscape, which could result in data breaches. Distributed Denial of Service (DDoS) assaults are major cyber-attacks among the many possible attacks because of their capacity to render services unusable by flooding systems with traffic. Strong DDoS detection technologies specifically designed for IoT are essential for the long-term growth of every sector that IoT serves. Since IoT devices frequently lack the built-in security features seen in more established computing platforms. As a consequence, DDoS analysis and defence are a growing area of research nowadays. The foundations of IoT, privacy and data security issues related to machine learning and IoT devices are reviewed in this paper. To limit our usage and understand the importance of protecting IoT devices in our lives, the paper also highlights current DDoS attacks and examines their effects on IoT devices. To defend against and lessen DDoS attacks on IoT devices, a strong authentication system built on machine learning techniques is needed. As a result, this review paper examines and reports on risk mitigation techniques for enhancing IoT adaptability as well as security and privacy issues.

For quicker reaction times, the data-driven infrastructure known as the Internet of Things (IoT) heavily relies on intelligent sensing devices. IoT devices are now susceptible to more expansive risk surfaces due to the changing cyber threats landscape, which could result in data breaches. Distributed Denial of Service (DDoS) assaults are major cyber-attacks among the many possible attacks because of their capacity to render services unusable by flooding systems with traffic. Strong DDoS detection technologies specifically designed for IoT are essential for the long-term growth of every sector that IoT serves. Since IoT devices frequently lack the built-in security features seen in more established computing platforms. As a consequence, DDoS analysis and defence are a growing area of research nowadays. The foundations of IoT, privacy and data security issues related to machine learning and IoT devices are reviewed in this paper. To limit our usage and understand the importance of protecting IoT devices in our lives, the paper also highlights current DDoS attacks and examines their effects on IoT devices. To defend against and lessen DDoS attacks on IoT devices, a strong authentication system built on machine learning techniques is needed. As a result, this review paper examines and reports on risk mitigation techniques for enhancing IoT adaptability as well as security and privacy issues.