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In many security domains, the ‘human in the system’ is often a critical line of defence in identifying, preventing and responding to any threats (Saikayasit, Stedmon, & Lawson, 2015). Traditionally, such security domains are often focussed on mainstream public safety within crowded spaces and border controls, through to identifying suspicious behaviours, hostile reconnaissance and implementing counter-terrorism initiatives. More recently, with growing insecurity around the world, organisations have looked to improve their security risk management frameworks, developing concepts which originated in the health and safety field to deal with more pressing risks such as terrorist acts, abduction and piracy (Paul, 2018). In these instances, security is usually the specific responsibility of frontline personnel with defined roles and responsibilities operating in accordance with organisational protocols (Saikayasit, Stedmon, Lawson, & Fussey, 2012; Stedmon, Saikayasit, Lawson, & Fussey, 2013). However, understanding the knowledge that frontline security workers might possess and use requires sensitive investigation in equally sensitive security domains.

This chapter considers how to investigate knowledge elicitation in these sensitive security domains and underlying ethics in research design that supports and protects the nature of investigation and end-users alike. This chapter also discusses the criteria used for ensuring trustworthiness as well as assessing the relative merits of the range of methods adopted.

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

This study aims to build a better understanding of researcher needs regarding support for data that you create, store, and/or manage using an electronic lab notebook (ELN), also referred to as electronic research notebook (ERN). The study also articulates the need for risk assessment for ELN products used by researchers for both open data and sensitive data that require standards.

The author used a participatory action research mixed-methods approach. A working group was formed from an ELN initial meeting. The working group team investigated several institutional ERN solutions by setting up trials, speaking with representatives from other research universities with ERN solutions and conducting internal and external research. This culminated in a broader-scale survey exploration.

Findings reveal there is no single institutional ELN license solution to satisfy all scientific disciplines. There is a need to develop foundational tools needed by all, provide additional tools and uses cases with best practices that can be tailored to various labs and research processes and develop a how-to guide on how to assemble the parts to create a useful ELN solution.

The research implications include providing support for researchers selecting an ERN solution through a combination of online guides, short tutorials and training. There is a need to develop foundational tools, uses cases with best practices that can be tailored to various labs and research processes and how-to guide on how to assemble the parts to create a useful hybrid-ELN solution.

Practical implications include aligning available ERN solutions with other institution provided technologies across the research life cycle to provide researchers a suite of tools to conduct and manage their research. Further investigating educational license discounts for courses using eLabJournal, RSpace, Protocols.io, Open Science Framework, LabArchives or other ERNs currently funded by student course fees via grant funded projects are key implications.

Social implications include the research computing environments of researchers that use ELN solutions approved through institutional risk assessment for open data are in compliance with university regulatory frameworks for use of the software in research.

The originality of this study includes risk assessments of ELNs solutions to better guide researchers in the selection process. To the best of the author’s knowledge, this survey was the first exploration of ELN on campus resulting in a final report to senior stakeholders. This study also highlights a developing grant proposal to further develop support across labs and campus.

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

According to the significant growth of literature and continued adoption of people analytics in practice, it has been promised that people analytics will inform evidence-based decision-making and improve business outcomes. However, existing people analytics literature remains underdeveloped in understanding whether and how such promises have been realized. Accordingly, this study aims to investigate the current reality of people analytics and uncover the debates and challenges that are emerging as a result of its adoption.

This study conducts a systematic literature review of peer-reviewed articles focused on people analytics published in the Association of Business School (ABS) ranked journals between 2011 and 2021.

The review illustrates and critically evaluates several emerging debates and issues faced by people analytics, including inconsistency among the concept and definition of people analytics, people analytics ownership, ethical and privacy concerns of using people analytics, missing evidence of people analytics impact and readiness to perform people analytics.

This review presents a comprehensive research agenda demonstrating the need for collaboration between scholars and practitioners to successfully align the promise and the current reality of people analytics.

This systematic review is distinct from existing reviews in three ways. First, this review synthesizes and critically evaluates the significant growth of peer-reviewed articles focused on people analytics published in ABS ranked journals between 2011 and 2021. Second, the study adopts a thematic analysis and coding process to identify the emerging themes in the existing people analytics literature, ensuring the comprehensiveness of the review. Third, this study focused and expanded upon the debates and issues evolving within the emerging field of people analytics and offers an updated agenda for the future of people analytics research.

The purpose of this paper is to develop a usable configuration management for Archistar, which utilizes secret sharing for redundantly storing data over multiple independent storage clouds in a secure and privacy-friendly manner. Selecting the optimal secret sharing parameters, cloud storage servers and other settings for securely storing the secret data shares, while meeting all of end user’s requirements and other restrictions, is a complex task. In particular, complex trade-offs between different protection goals and legal privacy requirements need to be made.

A human-centered design approach with structured interviews and cognitive walkthroughs of user interface mockups with system administrators and other technically skilled users was used.

Even technically skilled users have difficulties to adequately select secret sharing parameters and other configuration settings for adequately securing the data to be outsourced.

Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.

The authors present novel human computer interaction (HCI) guidelines for a usable configuration management, which propose to automatically set configuration parameters and to solve trade-offs based on the type of data to be stored in the cloud. Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.

Budgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.

We make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.

We identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.

The different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.

This article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital platforms in addressing data challenges and fostering data-driven innovation in the health sector.

For exploring the role of platforms, the authors propose a theoretical model based on the platform’s dynamic capabilities, assuming that, because of their set of capabilities, platforms may trigger innovation practices in actor interactions. To corroborate the theoretical framework, the authors present a detailed in-depth case study analysis of Apheris, an innovative data-driven digital platform operating in the healthcare scenario.

The paper finds that the innovative data-driven digital platform can be used to revolutionize established practices in the health sector (a) accelerating research and innovation; (b) overcoming challenges related to healthcare data. The case study demonstrates how data and intellectual property sharing can be privacy-compliant and enable new capabilities.

The paper attempts to fill the gap between the use of the data-driven digital platform and the critical innovation practices in the healthcare industry.