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This new paper aims to combine the recent new contributions about direct data driven control and other safety property to form an innovative direct data driven safety control for aircraft flight system. More specifically, within the framework of direct data driven strategy, the collected data are dealt with to get the identified plant and designed controller. After reviewing some priori information about aircraft flight system, a closed loop system with the unknown plant and controller simultaneously is considered. Data driven estimation is proposed to identify the plant and controller only through the ratios of two correlation functions, computed from the collected data. To achieve the dual missions about perfect tracking and safety property, a new notion about safety controller is introduced. To design this safety controller, direct data driven safety controller is proposed to solve one constrain optimization problem. Then the authors apply the Karush–Kuhn–Tucker (KKT) optimality conditions to derive the explicit safety controller.

First, consider one closed loop system corresponding to aircraft flight system with the unknown plant and feed forward controller, data driven estimation is used to identify the plant and feed forward controller. This identification process means nonparametric estimation. Second, to achieve the perfect tracking one given transfer function and guarantee the closed loop output response within one limited range simultaneously, safety property is introduced. Then direct data driven safety control is proposed to design the safety controller, while satisfying the dual goals. Third, as the data driven estimation and direct data driven safety control are all formulated as one constrain optimization problem, the KKT optimality conditions are applied to obtain the explicit safety controller.

Some aircraft system identification and aircraft flight controller design can be reformulated as their corresponding constrain optimization problems. Then through solving these constrain optimization problems, the optimal estimation and controller are yielded, while satisfying our own priori goals. First, data driven estimation is proposed to get the rough estimation about the plant and controller. Second, data driven safety control is proposed to get one safety controller before our mentioned safety concept.

To the best of the authors’ knowledge, some existing theories about nonparametric estimation and tube model predictive control are very mature, but few contributions are applied in practice, such as aircraft system identification and aircraft flight controller design. This new paper shows the new theories about data driven estimation and data driven safety control on aircraft, being corresponded to the classical nonparametric estimation and tube model predictive control. Specifically, data driven estimation gives the rough estimations for the aircraft and its feed forward controller. Furthermore, after introducing the safety concept, data driven safety control is introduced to achieve the desired dual missions with the combination of KKT optimality conditions.

In real-time entertainment processing applications, processing of the multiple data streams demands high efficient multiple transfers, which leads to the computational overhead for system-on-chip (SoC), which runs the artificial intelligence algorithms. High-performance direct memory access controller (DMAC) is incorporated in SoC to perform the multiple data transfers without the participation of main processors. But achieving the area-efficient and power-aware DMAC suitable for streaming the multiple data remains to be a daunting challenge among the researchers.

The purpose of this paper to provide the DMA operations without intervention of central processing unit (CPU) for bulk video data transmissions.

The proposed DMAC has been developed based on the hybrid advanced extensible interface (AXI)-PCI bus subsystem to handle the multiple data streams from the video sources. The proposed model consists of bus selector module, user control signal, status register, DMA-supported address and AXI-PCI subsystems to achieve better performance in analysing the video frames.

The extensive experimentation is carried out with Xilinx Zynq SoC architecture using Very High Speed integrated circuit hardware description language (VHDL) programming, and performance metrics such as utilization area and power are calculated and compared with the other existing DMA controllers such as Scatter-DMA, Gather-DMA and Enhanced DMA. Simulation results demonstrate that the proposed DMAC has outperformed other existing DMAC in terms of less area, less delay and power, which makes the proposed model suitable for streaming multiple video streams.

This chapter analyzes the compliance of some category of Open Data in Politics with EU General Data Protection Regulation (GDPR) requirements. After clarifying the legal basis of this framework, with specific attention to the processing procedures that conform to the legitimate interests pursued by the data controller, including open data licenses or anonymization techniques, that can result in partial application of the GDPR, but there is no generic guarantee, and, as a consequence, an appropriate process of analysis and management of risks is required.

The enforcement of the General Data Protection Regulation imposes specific privacy- and -security related requirements that any organisation that processes European Union citizens’ personal data must comply with. The application of privacy- and security-by-design principles are assisting organisation in achieving compliance with the Regulation. The purpose of this study is to assist data controllers in their effort to achieve compliance with the new Regulation, by proposing the adoption of the privacy level agreement (PLA). A PLA is considered as a formal way for the data controllers and the data subjects to mutually agree the privacy settings of a service provisioned. A PLA supports privacy management, by analysing privacy threats, vulnerabilities and information systems’ trust relationships.

However, the concept of PLA has only been proposed on a theoretical level. To this aim, two different domains have been selected acting as real-life case studies, the public administration and the health care, where special categories of personal data are processed.

The results of the evaluation of the adoption of the PLA by the data controllers are positive. Furthermore, they indicate that the adoption of such an agreement facilitates data controllers in demonstrating transparency of their processes. Regarding data subjects, the evaluation process revealed that the use of the PLA increases trust levels on data controllers.

This paper proposes a novel reference architecture to enable PLA management in practice and reports on the application and evaluation of PLA management.

The purpose of this paper is to give a brief guidance on what a cloud provider should consider and what further actions to take to comply with General Data Protection Regulation (GDPR).

This paper presents in detail the requirements for GDPR compliance of cloud computing environments, presents the GDPR roles (data controller and data processor) in a cloud environment and discusses the applicability of GDPR compliance requirements for each cloud architecture (Infrastructure as a Service, Platform as a Service, Software as a Service), proposes countermeasures for satisfying the aforementioned requirements and demonstrates the applicability of the aforementioned requirements and countermeasures to a PaaS environment offering services for building, testing, deploying and managing applications through cloud managed data centers. The applicability of the method has been demonstrated on in a PaaS environment that offers services for building, testing, deploying and managing applications through cloud managed data centers.

The results of the proposed GDPR compliance measures for cloud providers highlight the effort and criticality required from cloud providers to achieve compliance.

Purpose: This chapter sets out to lay out and analyse the effectiveness of the General Data Protection Regulation (GDPR), a recently established European Union (EU) regulation, in the local insurance industry.

Methodology: This was done through a systematic literature review to determine what has already been done and then a survey as a primary research tool to gather information. The survey was aimed at clients and employees of insurance entities.

Findings: The general results are that effectiveness can be segmented into different factors and vary regarding the respondents’ confidence. Other findings include that the GDPR has increased costs, and its expectations are unclear. These findings suggest that although the GDPR was influential in the insurance market, some issues about this regulation still exist.

Conclusions: GDPR fulfils its purposes; however, the implementation process of this regulation can be facilitated if better guidelines are issued for entities to follow to understand its expectations better and follow the law and fulfil its purposes most efficiently.

Practical implications: These conclusions imply that the GDPR can be improved in the future. Overall, as a regulation, it is suitable for the different member states of the EU, including small states like Malta.

The paper posits that a solution for businesses to use privacy-friendly data repositories for its customers’ data is to change from the traditional centralized repository to a trusted, decentralized data repository. Blockchain is a technology that provides such a data repository. However, the European Union’s General Data Protection Regulation (GDPR) assumed a centralized data repository, and it is commonly argued that blockchain technology is not usable. This paper aims to posit a framework for adopting a blockchain that follows the GDPR.

The paper uses the Levy and Ellis’ narrative review of literature methodology, which is based on constructivist theory posited by Lincoln and Guba. Using five information systems and computer science databases, the researchers searched for studies using the keywords GDPR and blockchain, using a forward and backward search technique. The search identified a corpus of 416 candidate studies, from which the researchers applied pre-established criteria to select 39 studies. The researchers mined this corpus for concepts, which they clustered into themes. Using the accepted computer science practice of privacy by design, the researchers combined the clustered themes into the paper’s posited framework.

The paper posits a framework that provides architectural tactics for designing a blockchain that follows GDPR to enhance privacy. The framework explicitly addresses the challenges of GDPR compliance using the unimagined decentralized storage of personal data. The framework addresses the blockchain–GDPR tension by establishing trust between a business and its customers vis-à-vis storing customers’ data. The trust is established through blockchain’s capability of providing the customer with private keys and control over their data, e.g. processing and access.

The paper provides a framework that demonstrates that blockchain technology can be designed for use in GDPR compliant solutions. In using the framework, a blockchain-based solution provides the ability to audit and monitor privacy measures, demonstrates a legal justification for processing activities, incorporates a data privacy policy, provides a map for data processing and ensures security and privacy awareness among all actors. The research is limited to a focus on blockchain–GDPR compliance; however, future research is needed to investigate the use of the framework in specific domains.

The paper posits a framework that identifies the strategies and tactics necessary for GDPR compliance. Practitioners need to compliment the framework with rigorous privacy risk management, i.e. conducting a privacy risk analysis, identifying strategies and tactics to address such risks and preparing a privacy impact assessment that enhances accountability and transparency of a blockchain.

With the increasingly strategic use of data by businesses and the contravening growth of data privacy regulation, alternative technologies could provide businesses with a means to nurture trust with its customers regarding collected data. However, it is commonly assumed that the decentralized approach of blockchain technology cannot be applied to this business need. This paper posits a framework that enables a blockchain to be designed that follows the GDPR; thereby, providing an alternative for businesses to collect customers’ data while ensuring the customers’ trust.

This paper, through examining the judgment on Case C-131/12 and the European Union (EU)’s Proposal for a General Data Protection Regulation, aims to demonstrate to the records management (RM) profession, the importance of being proactively involved in records creation identification and the challenges of performing sound retention analyses for newly emerging activities. It also serves as a call to the RM profession that more active participation in law-making processes is needed.

The research selects the current right to be forgotten phenomenon as an illuminating case and examines it with fundamental RM concepts and principles, in particular those relating to records creation and retention. The research process consists of three major parts: one, the establishment of an analytical framework based on RM theories; two, description of the selected case that is relevant to the analysis; and three, the application of the analytical framework to the described case.

Records retentions are much needed for the activities of data controllers that are now established by the most recent Judgment of the European Court of Justice pertinent to the right to be forgotten and the proposed General Data Protection Regulation. The determination of retention periods for such activities requires an RM framework that synthesizes the identification of digital records and the various types of value associated with the different usages of records. It is also observed that the data protection legal framework does not address RM considerations, or at least, not in any explicit, easily recognizable manners.

Records retentions are much needed for the activities of data controllers and/or processors that are now required by the Judgment of the European Court of Justice and the proposed EU General Data Protection Regulation, yet the legal framework does not offer any assistance in establishing retentions. It is also observed that the data protection legal framework fully acknowledges the importance of records but fails to address RM considerations – at least, not in any explicit, easily recognizable manners.

The findings are expected to be instructive to data controllers and/or processors, in particular with respect to records creation identification and records retention establishment in their organizations. It is also expected that the observations generated during the analysis process could shed light on the development of the RM profession.

The right to be forgotten in the digital world has newly acquired complications, and it has the potential to affect not just the privacy right but also the rights considered conflicting to it, such as the rights of freedom of press and freedom of expression/speech. Efficient and effective RM programs should be able to assist their parent organizations in dealing with this complicated situation through creating and managing records that support the compliance of regulatory requirements on the one hand and the balancing of competing rights on the other hand.

The research appears to be the first of its kind according to the literature search conducted within the accessibility scope of the researcher.

This paper seeks to present a feedback error learning neuro‐controller for an unstable research helicopter.

Three neural‐aided flight controllers are designed to satisfy the ADS‐33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non‐linearity and parameter uncertainties. The basic building block of the neuro‐controller is a nonlinear auto regressive exogenous (NARX) input neural network. For each neural controller, the parameter update rule is derived using Lyapunov‐like synthesis. An offline finite time training is used to provide asymptotic stability and on‐line learning strategy is employed to handle parameter uncertainty and nonlinearity.

The theoretical results are validated using simulation studies based on a nonlinear six degree‐of‐freedom helicopter undergoing an agile maneuver. The neural controller performs well in disturbance rejection is the presence of gust and sensor noise.

The neuro‐control approach presented in this paper is well suited to unmanned and small‐scale helicopters.

The study shows that the neuro‐controller meets the requirements of ADS‐33 handling qualities specifications of a helicopter.

The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.

A linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.

Open/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.

(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T² statistic for controller performance monitoring.