text: 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.
  @cite_61: Edge processing in IoT networks offers the ability to enforce privacy at the point of data collection. However, such enforcement requires extra processing in terms of data filtering and the ability to configure the device with knowledge of policy. Supporting this processing with Cloud resources can reduce the burden this extra processing places on edge processing nodes and provide a route to enable user defined policy. Research from the PaaSage project [12] on Cloud modelling language is applied to IoT networks to support IoT and Cloud integration linking the worlds of Cloud and IoT in a privacy protecting way.
  @cite_67: Data are today an asset more critical than ever for all organizations we may think of. Recent advances and trends, such as sensor systems, IoT, cloud computing, and data analytics, are making possible to pervasively, efficiently, and effectively collect data. However such pervasive data collection and the lack of security for IoT devices increase data privacy concerns. In this paper, we discuss relevant concepts and approaches for data privacy in IoT, and identify research challenges that must be addressed by comprehensive solutions to data privacy.
  @cite_32: This paper addresses privacy issues in managing electronic health records by a third party cloud based service. Compared to traditional authentication-authorization mechanisms, the proposed approach minimizes the leakage of identity information of involved participants through unlinkability. Furthermore, it gives the ability to health record owners for making access control decisions. This solution employs an identity management scheme that enhances consumer privacy by preventing consumer profiling based on the credentials used to satisfy the service provider policies. The paper proposes a set of mechanisms to allow authenticated unlinkable access to electronic health records, while giving the record owners ability to make access control decisions. The security evaluation for accessing data in the cloud is detailed, and the implementation of the system is evaluated in this paper.
  @cite_84: Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.
  @cite_44: Healthcare and tourism are among the fastest growing business domains in the world. These are biggest service industries that affect whole world population and give jobs to millions of people. Recently we witness major changes in both industries, as more services are transferred to small providers, including individual entrepreneurs and SMEs. This process is driven by huge growth in demand, which cannot be fulfilled by applying traditional solutions. The new generation of digital services reshapes landscape of both industries. Some players see it as a threat, as digital services replace their traditional business models. But fighting against progress is useless, especially when you cannot fulfill growing by old means. Internet of Things (IoT) is an integral part of the Future Internet ecosystem that will have major impact on development of healthcare and e-Tourism services. IoT provides an infrastructure to uniquely identify and link physical objects to their virtual representations in Internet. As a result any physical object can have virtual reflection in the service space. This gives an opportunity to replace actions on physical objects by operations on their virtual reflections, which can be done much faster, cheaper and more comfortable for people. This provides a huge space for developing and applying new business models. In this paper we summarize research and development results of IoT studies and discuss ideas on how to apply them to business.
  @cite_40: Within the last decade, Security became a major focus in the traditional IT-Industry, mainly through the interconnection of systems and especially through the connection to the Internet. This opened up a huge new attack surface, which resulted in major takedowns of legitimate services and new forms of crime and destruction. This led to the development of a multitude of new defense mechanisms and strategies, as well as the establishing of Security procedures on both, organizational and technical level. Production systems have mostly remained in isolation during these past years, with security typically focused on the perimeter. Now, with the introduction of new paradigms like Industry 4.0, this isolation is questioned heavily with Physical Production Systems (PPSs) now connected to an IT-world resulting in cyber-physical systems sharing the attack surface of traditional web based interfaces while featuring completely different goals, parameters like lifetime and safety, as well as construction. In this work, we present an outline on the major security challenges faced by cyber-physical production systems. While many of these challenges harken back to issues also present in traditional web based IT, we will thoroughly analyze the differences. Still, many new attack vectors appeared in the past, either in practical attacks like Stuxnet, or in theoretical work. These attack vectors use specific features or design elements of cyber-physical systems to their advantage and are unparalleled in traditional IT. Furthermore, many mitigation strategies prevalent in traditional IT systems are not applicable in the industrial world, e.g., patching, thus rendering traditional strategies in IT-Security unfeasible. A thorough discussion of the major challenges in CPPS-Security is thus required in order to focus research on the most important targets.
  @cite_83: 
  @cite_74: With the ever increasing number of connected devices and the over abundance of data generated by these devices, data privacy has become a critical concern in the Internet of Things (IoT). One promising privacy-preservation approach is Attribute-Based Encryption (ABE), a public key encryption scheme that enables fine-grained access control, scalable key management and flexible data distribution. This paper presents an in-depth performance evaluation of ABE that focuses on execution time, data and network overhead, energy consumption, and CPU and memory usage. We evaluate two major types of ABE, Key-Policy Attribute-Based Encryption (KP-ABE) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), on different classes of mobile devices including a laptop and a smartphone. To the best of our knowledge, this is the first comprehensive study of ABE dedicated solely to its performance. Our results provide insights into important practical issues of ABE, including what computing resources ABE requires in heterogeneous environments, at what cost ABE offers benefits, and under what situations ABE is best suited for use in the IoT.
  @cite_66: The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza.
summary: 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.