Computational Governance and Violable Contracts for Blockchain Applications

We propose a sociotechnical, yet computational, approach to building decentralized applications that accommodates and exploits blockchain technology. Our architecture incorporates the notion of a declarative, violable contract and enables flexible governance based on formal organizational structures, correctness verification without obstructing autonomy, and a basis for trust

Smart Contracts to Support the Advancement of Blockchain Technology in the Security Integrity

The development of technology today is used as a benchmark in the advancement of the industrial world where the development of technology has influenced various aspects in the life of today's society. Smart contracts as one form of blockchain technology that resembles a conventional contract can be used to bind agreements between one party and another. One difference between a smart contract and a conventional contract is the smart contract that is stored in the blockchain. With the presence of smart contracts on the blockchain has become one of the most sought-after technologies, because the number of users is high enough for each transaction within the company. In this case various features of smart contracts applications in various worlds, ranging from financial services, life sciences, energy resources and media voting. Smart contracts still pose a lot of challenges that overwhelm the interaction of some Parties, such as users, developers, and organizations built on smart contracts. Smart contracts are essentially a very effective source of problem solvers, where smart contracts on the blockchain make it easy to maintain data security, and save costs and time. In addition, in the absence of third parties strongly minimizes the fraud that is often done by irresponsible parties, this prevents conflicts between parties. Prone to cases of loss of a document is generated because there is no secure storage media. The advent of smart contracts on the blockchain is expected to be a solution to tackle most of the world's commercial and bureaucratic systems. &nbsp

Interaction-Oriented Software Engineering:Programming abstractions for autonomy and decentralization

We review the main ideas and elements of Interaction-Oriented Software Engineering (IOSE), a program of research that we have pursued for the last two decades, a span of time in which it has grown from philosophy to practical programming abstractions. What distinguishes IOSE from any other program of research is its emphasis on supporting autonomy by modeling the meaning of communication and using that as the basis for engineering decentralized sociotechnical systems. Meaning sounds esoteric but is the basis for practical decision making and a holy grail for the field of distributed systems. We describe our contributions so far, directions for research, and the potential for broad impact on computing

Towards a digital ethics: EDPS ethics advisory group

The EDPS Ethics Advisory Group (EAG) has carried out its work against the backdrop of two significant social-political moments: a growing interest in ethical issues, both in the public and in the private spheres and the imminent entry into force of the General Data Protection Regulation (GDPR) in May 2018. For some, this may nourish a perception that the work of the EAG represents a challenge to data protection professionals, particularly to lawyers in the field, as well as to companies struggling to adapt their processes and routines to the requirements of the GDPR. What is the purpose of a report on digital ethics, if the GDPR already provides all regulatory requirements to protect European citizens with regard to the processing of their personal data? Does the existence of this EAG mean that a new normative ethics of data protection will be expected to fill regulatory gaps in data protection law with more flexible, and thus less easily enforceable ethical rules? Does the work of the EAG signal a weakening of the foundation of legal doctrine, such as the rule of law, the theory of justice, or the fundamental values supporting human rights, and a strengthening of a more cultural approach to data protection? Not at all. The reflections of the EAG contained in this report are not intended as the continuation of policy by other means. It neither supersedes nor supplements the law or the work of legal practitioners. Its aims and means are different. On the one hand, the report seeks to map and analyse current and future paradigm shifts which are characterised by a general shift from analogue experience of human life to a digital one. On the other hand, and in light of this shift, it seeks to re-evaluate our understanding of the fundamental values most crucial to the well-being of people, those taken for granted in a data-driven society and those most at risk. The objective of this report is thus not to generate definitive answers, nor to articulate new norms for present and future digital societies but to identify and describe the most crucial questions for the urgent conversation to come. This requires a conversation between legislators and data protection experts, but also society at large - because the issues identified in this report concern us all, not only as citizens but also as individuals. They concern us in our daily lives, whether at home or at work and there isn’t a place we could travel to where they would cease to concern us as members of the human species

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

Big data use at an automotive manufacturer: a framework to address privacy concerns in Hadoop Technology.

An automotive manufacturer can generate big data through accessible data points from internal and external Internet of Things (IoT) data sources connected to the production line. Big data analytics needs to be applied to these large and complex datasets to realise the associated opportunities, such as an improved manufacturing process, optimised supply chain management, competitive advantage and business growth. In order to store, manage and process the data, automotive manufacturers are using Apache Hadoop technology. Apache Hadoop is a cost-effective, scalable, and fault-tolerant technology. However, there has been a concern raised regarding the privacy of big data in Apache Hadoop. A key challenge in Hadoop technology is its ineffective security model, making the data susceptible to unauthorised users. Consequently, a breach in data privacy results in automotive manufacturers becoming victims of theft of trade secrets and intellectual property via corporate spies. This theft has a negative impact and results in the loss of company reputation, business competitiveness and business growth in the automotive market. This study investigated a solution to ensure big data privacy when using Hadoop technology. The Selective Organisational Information Privacy and Security Violations Model (SOIPSVM) and the Capability Maturity Model (CMM) provided the theoretical base for this study. The researcher undertook a literature analysis and qualitative study to understand and address the identified research problem. The primary data was collected from ten Information Technology (IT) specialists at a local automotive manufacturer. These specialists participated in an interview session, which also included the completion of a questionnaire. All questions were pre-determined and open-ended, and the participants' responses were recorded. Primary data was analysed using the inductive approach by identifying relevant themes and sub-themes. In contrast, the literature analysis included academic journals, conference proceedings, websites, and books, which were critically discussed in this study. This study's findings indicated various measures to be implemented by the automotive manufacturer to address the research problem. Critical success factors were derived from the identified measures, which addressed significant data privacy issues in using Hadoop technology. The identified critical success factors included: control of internal and external data sources; monitor the value of big data towards improving the automotive manufacturing process and user behaviour; implementation of user authentication; encryption to secure data; disaster recovery and backup plan; execution of authorisation and Access Control List (ACLS); conduct audits and regular reviews of user access to data; apply data masking to sensitive data and tokenization to secure data; build own infrastructure to store and analyse data; install regular security updates and update passwords regularly. Each factor had a purpose that examined big data management, governance and compliance in detail. The identified factors contributed towards ensuring data privacy in the use of Hadoop technology. These factors were categorised into contextual and rule and regulatory conditions adopted from the SOIPSVM. Identified conditions were then aligned to the five-level CMM. Each condition was expanded upon at various maturity levels to form a framework that addressed the main research problem. The framework's application was described as an independent assessment of each critical success factor and provided a guide through various maturity levels. The framework's purpose was to address and overcome big data privacy concerns in using Hadoop technology at a local automotive manufacturer.Thesis (MCom) (Information Systems) -- University of Fort Hare, 202

Big data use at an automotive manufacturer: a framework to address privacy concerns in Hadoop Technology.

An automotive manufacturer can generate big data through accessible data points from internal and external Internet of Things (IoT) data sources connected to the production line. Big data analytics needs to be applied to these large and complex datasets to realise the associated opportunities, such as an improved manufacturing process, optimised supply chain management, competitive advantage and business growth. In order to store, manage and process the data, automotive manufacturers are using Apache Hadoop technology. Apache Hadoop is a cost-effective, scalable, and fault-tolerant technology. However, there has been a concern raised regarding the privacy of big data in Apache Hadoop. A key challenge in Hadoop technology is its ineffective security model, making the data susceptible to unauthorised users. Consequently, a breach in data privacy results in automotive manufacturers becoming victims of theft of trade secrets and intellectual property via corporate spies. This theft has a negative impact and results in the loss of company reputation, business competitiveness and business growth in the automotive market. This study investigated a solution to ensure big data privacy when using Hadoop technology. The Selective Organisational Information Privacy and Security Violations Model (SOIPSVM) and the Capability Maturity Model (CMM) provided the theoretical base for this study. The researcher undertook a literature analysis and qualitative study to understand and address the identified research problem. The primary data was collected from ten Information Technology (IT) specialists at a local automotive manufacturer. These specialists participated in an interview session, which also included the completion of a questionnaire. All questions were pre-determined and open-ended, and the participants' responses were recorded. Primary data was analysed using the inductive approach by identifying relevant themes and sub-themes. In contrast, the literature analysis included academic journals, conference proceedings, websites, and books, which were critically discussed in this study. This study's findings indicated various measures to be implemented by the automotive manufacturer to address the research problem. Critical success factors were derived from the identified measures, which addressed significant data privacy issues in using Hadoop technology. The identified critical success factors included: control of internal and external data sources; monitor the value of big data towards improving the automotive manufacturing process and user behaviour; implementation of user authentication; encryption to secure data; disaster recovery and backup plan; execution of authorisation and Access Control List (ACLS); conduct audits and regular reviews of user access to data; apply data masking to sensitive data and tokenization to secure data; build own infrastructure to store and analyse data; install regular security updates and update passwords regularly. Each factor had a purpose that examined big data management, governance and compliance in detail. The identified factors contributed towards ensuring data privacy in the use of Hadoop technology. These factors were categorised into contextual and rule and regulatory conditions adopted from the SOIPSVM. Identified conditions were then aligned to the five-level CMM. Each condition was expanded upon at various maturity levels to form a framework that addressed the main research problem. The framework's application was described as an independent assessment of each critical success factor and provided a guide through various maturity levels. The framework's purpose was to address and overcome big data privacy concerns in using Hadoop technology at a local automotive manufacturer.Thesis (MCom) (Information Systems) -- University of Fort Hare, 202