Guest editors' introduction : Special issue on trust, security, and privacy in parallel and distributed systems

In modern computing paradigms, most computing systems, e.g., cluster computing, grid computing, cloud computing, the Internet, telecommunication networks, Cyber- Physical Systems (CPS), and Machine-to-Machine communication networks (M2M), are parallel and distributed systems. While providing improved expandability, manageability, efficiency, and reliability, parallel and distributed systems increase their security weaknesses to an unprecedented scale. As the system devices are widely connected, their vulnerabilities are shared by the entire system. Because tasks are allocated to, and information is exchanged among the system devices that may belong to different users, trust, security, and privacy issues have yet to be resolved. This special issue of the IEEE Transactions on Parallel and Distributed Systems (TPDS) highlights recent advances in trust, security, and privacy for emerging parallel and distributed systems. This special issue was initiated by Dr. Xu Li, Dr. Patrick McDaniel, Dr. Radha Poovendran, and Dr. Guojun Wang. Due to a large number of submissions, Dr. Zhenfu Cao, Dr. Keqiu Li, and Dr. Yang Xiang were later invited to the editorial team. Dr. Xu Li was responsible for coordinating the paper review process. In response to the call for papers, we received 150 effective submissions, out of which 24 are included in this special issue after rigorous review and careful revision, presenting an acceptance ratio of 16 percent. The accepted papers are divided into three groups, covering issues related to trust, security, and privacy, respectively.

Trust enhanced security in SaaS cloud computing

Trust problem in Software as a Service Cloud Computing is a broad range of a Data Owner’s concerns about the data in the Cloud. The Data Owner’s concerns about the data arise from the way the data is handled in locations and machines that are unknown to the Data Owner.

Review of trust and machine ethics research: towards a bio-inspired computational model of ethical trust (CMET)

Recent advances in the fields of robotics, cyborg development, moral psychology, trust, multi agent-based systems and socionics have raised the need for a better understanding of ethics, moral reasoning, judgment and decision-making within the system of man and machines. Here we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the social moral norms at the collective end. We review salient works in the fields of trust and machine ethics research, underscore the importance and the need for a deeper understanding of ethical trust at the individual level and the development of collective social moral norms. Drawing upon the recent findings from neural sciences on mirror-neuron system (MNS) and social cognition, we present a bio-inspired Computational Model of Ethical Trust (CMET) to allow investigations of the interplay of ethical trust and social moral norms.

Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Interplay of ethical trust and social moral norms : environment modelling and computational mechanisms in agent-based social simulation

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and social moral norms addresses that challenge via an analytical framework on the spread of moral norms, the modelling of social environment and the selection of spread mechanisms as applied to agent-based social simulation. We describe the mechanism alignment mapping, two forms of interaction modelling between the social environment and agents, and the results obtained from the simulation of our computational model. These results provide an insight into how the agent-based paradigm can be applied as a technique of investigation for normative moral processes in computational social sciences.

Police Misconduct as a Breach of Public trust: the Offence of Misconduct in Public Office

Until relatively recently, the common law offence of misconduct in public office has been regarded as anachronistic. The offence was perceived to have been supplanted by specific statutory offences that could more appropriately deal with criminal conduct by public officials. However, there has been a revival of the offence with successful prosecutions occurring in Australia, England and Hong Kong. Many of these contemporary cases have involved police officers. Examination of these cases reveals that the circumstances in which misconduct in public office has been identified have been diverse, including the unauthorised disclosure of confidential information, the use of false search warrants and the sexual exploitation of vulnerable persons. In many instances, police officers were charged with other criminal offences in addition to charges relating to misconduct in public office. The matters prosecuted as misconduct in public office typically involved matters that were serious and/or could not be adequately prosecuted as other criminal offences or as breaches of police regulations governing conduct. Consequently, despite the proliferation of statutory criminal offences in the 20th century it appears that there continues to be a place for the offence of misconduct in public office. It criminalises misconduct by police officers that may not be adequately dealt with by other offences and recognises the public trust dimension of wrongdoing by these officials. However, a continuing and fundamental challenge is to determine the appropriate definition and scope of the offence.

Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data

In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks. © 2014 Springer Science+Business Media Singapore.

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.