Solving the disclosure auditing problem for secondary cell suppression by means of linear programming

National Statistical Institutes (NSIs) have the obligation to protect the privacy of individual persons or enterprises against disclosure of potentially sensitive information. For this reason, NSIs protect tabular data against disclosure of sensitive information before they are released. For tabular magnitude data, the starting point of this protection process usually is a sensitivity measure for individual cells. Such a sensitivity measure defines when a cell value is considered safe for publication or not. An often used method to protect a table with unsafe cells against disclosure of sensitive information is cell suppression. [5] argues that the standard criterion for deciding whether a table after suppression is safe or not is somewhat inconsistent and proposes a new criterion. [5] also gives a mixed-integer programming problem formulation for applying this new criterion. The problem with that formulation is that it is quite large and very hard to solve for even moderately sized tables. To be more precise, that mixed-integer programming problem formulation suggests that the auditing problem based on the criterion of [5] is NP-hard. The general assumption among operations research experts is that the computing time for NP-hard problems is non-polynomial in their input parameters. In the current paper, we propose solving a number of smaller and computationally much easier linear programming problems instead of solving one large mixed-integer programming problem. Solving linear programming problems can be done in time polynomial in their input parameter

Partnering to combat corruption in infrastructure services: a toolkit

Problems with corruption have long been recognized as key constraints to the development of sustainable infrastructure services. The objective of this Toolkit is to propose a framework and tools geared to understanding, exploring and acting on corruption in the delivery of services. The scope of the work covers infrastructure services in urban and rural areas of developed and developing countries. A number of Toolkits on corruption have been published in recent years; however, to date, these have not been focused on the infrastructure sector or the impacts of corruption on the poor. This Toolkit is intended to fill that gap. The Toolkit is cross-sectoral in its approach, making it of relevance to those working on water supply, sanitation, drainage, roads and paving, transport, solid waste management, street lighting and housing sectors. This Toolkit brings together, in a systematic way, a variety of individual tools, which support the process of combating corruption in infrastructure services. The tools themselves are synthesized from real world experience; derived from a review of literature, desk-based case surveys and country case studies. These are not academic concepts, but genuinely operational tools. This Toolkit avoids taking a blueprint or top-down approach, but rather takes the perspective of operators, regulators and service users, especially the poor. By taking these tools, and relating them systematically to various aspects of combating corruption, this Toolkit should fulfil the urgent need expressed by policy makers, professional staff, regulators and consumers

Accounting, paper shadows and the stigmatised poor

The social implications of accounting are explored through an historical study of spoiled identities in state welfare systems. The processing, recording, classification and communication inherent in the accounting practices deployed in such systems have the potential to (re)construct identities, inform perceptions of self and impact on the social relationships of the welfare claimant. The paper examines these potentialities through an investigation of the accounting regime attending the system of poor relief in Victorian England and Wales. Informed primarily by the work of Goffman it is suggested that accounting processes comprised degradation ceremonies which compounded the stigmatisation of the recipient of relief, accounting classifications served to inscribe existing and create additional spoiled identities of the pauper, and individualized forms of accounting disclosure compromised the management of stigma by the poor

Regulating modern slavery in global supply chains: exploring the design and effectiveness of national legislation

The widespread fragmentation of global supply chains (GSCs) poses considerable challenges for regulating severe forms of labour exploitation, collectively referred to as modern slavery. Recent responses to these challenges include the promulgation of modern slavery reporting (MSR) laws in the UK, California and Australia. In theory, these laws are a promising development geared towards leveraging corporate influence over GSCs to minimise, or where possible, eliminate modern slavery. However, commentators have criticised their design for lack of sanctions and inadequate reporting requirements. Despite this, MSR laws continue to proliferate internationally, underscoring a need for critical inquiry into the antecedents of their design and their practical effectiveness. To that end, this thesis poses the following overarching research question: How do policy-making process dynamics shape the design of modern slavery reporting laws, and how do external stakeholder pressure and actors internal to firms influence their effectiveness? To answer this question the thesis is organised into six chapters. Two introductory chapters define modern slavery and explore the literature on regulatory design. Following this, three empirical studies are presented, each occupying a single chapter. These studies apply policy change theory to explain MSR law design outcomes, stakeholder influence over reporting outcomes, and the how actors internal to the firm shape changes in policy and practice, respectively. The final chapter summarises the central conceptual and practice-related insights of the thesis. Regarding design, MSR law design is shown to be shaped primarily by coalitions of actors through deliberative, associational and ideological strategies throughout the policy change process. Regarding effectiveness, reporting quality is found to depend on the influence of various stakeholder groups, which varies significantly across institutional settings. In addition, policy and practice changes prompted by MSR laws are shown to be contingent on the extent to which organisations empower internal compliance professionals to promote and enact change and how third-party advisers define best practice. Overall, the findings of this study indicate that fulfilment of the aims of MSR laws is far from guaranteed. This will require changes to their regulatory design and more regulator commitment to steering corporate behaviour in the desired direction

The Right to Vote Securely

American elections currently run on outdated and vulnerable technology. Computer science researchers have shown that voting machines and other election equipment used in many jurisdictions are plagued by serious security flaws, or even shipped with basic safeguards disabled. Making matters worse, it is unclear whether current law requires election authorities or companies to fix even the most egregious vulnerabilities in their systems, and whether voters have any recourse if they do not. This Article argues that election law can, does, and should ensure that the right to vote is a right to vote securely. First, it argues that constitutional voting rights doctrines already prohibit election practices that fail to meet a bare minimum threshold of security. But the bare minimum is not enough to protect modern election infrastructure against sophisticated threats. This Article thus proposes new statutory measures to bolster election security beyond the constitutional baseline, with technical provisions designed to change the course of insecure election practices that have become regrettably commonplace, and to standardize best practices drawn from state-of-the-art research on election security

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Salford postgraduate annual research conference (SPARC) 2012 proceedings

These proceedings bring together a selection of papers from the 2012 Salford Postgraduate Annual Research Conference (SPARC). They reflect the breadth and diversity of research interests showcased at the conference, at which over 130 researchers from Salford, the North West and other UK universities presented their work. 21 papers are collated here from the humanities, arts, social sciences, health, engineering, environment and life sciences, built environment and business