published_or_final_version / Journalism and Media Studies Centre / Master / Master of Journalism

Modern organizations (e.g., hospitals, banks, social networks, search engines) hold large volumes of personal information, and rely heavily on auditing for enforcement of privacy policies. These audit mechanisms combine automated methods with human input to detect and punish violators. Since human audit resources are limited, and often not sufficient to investigate all potential violations, current state-of-the -art audit tools provide heuristics to guide human effort. However, numerous reports of privacy breaches caused by malicious insiders bring to question the effectiveness of these audit mechanisms. Our thesis is that effective audit resource allocation and punishment levels can be efficiently computed by modeling the audit process as a game between a rational auditor and a rational or worst-case auditee. We present several results in support of the thesis. In the worst-case adversary setting, we design a game model taking into account organizational cost of auditing and loss from violations. We propose the notion of low regret as a desired audit property and provide a regret minimizing audit algorithm that outputs an optimal audit resource allocation strategy. The algorithm improves upon prior regret bounds in the partial information setting. In the rational adversary setting, we enable punishments by the auditor, and model the adversary's utility as a trade-off between the benefit from violations and loss due to punishment when detected. Our Stackelberg game model generalizes an existing deployed security game model with punishment parameters. It applies to natural auditing settings with multiple auditors where each auditor is restricted to audit a subset of the potential violations. We provide novel polynomial time algorithms to approximate the non-convex optimization problem used to compute the Stackelberg equilibrium. The algorithms output optimal audit resource allocation strategy and punishment levels. We also provide a method to reduce the optimization problem size, achieving up to 5x speedup for realistic instances of the audit problem, and for the related security game instances.

The purpose of this research is to understand factors contributing to successes and challenges in community participation especially in waste minimization initiatives; in order to inform policies and contribute to improve the design of the initiative. The objectives of the research are: firstly, to understand the current state of public participation in waste minimization at Emfuleni Local Municipality. Secondly, it is to determine the extent of willingness of the community to participate in waste minimization initiatives. Thirdly, to determine strategies on how to promote public participation in waste minimization. Fourthly, to identify constraints and challenges of public participation in waste minimization and what kind of support is required for the community to participate in waste minimization initiatives at Emfuleni Local Municipality. Respondents were drawn from fifty households within the community of Bophelong Extension 13, Emfuleni Recycling groups, Waste Management officials, Gauteng Department of Agriculture, Conservation and Environment stake holder participation unit and Waste Buyers in Vanderbijlpark. The researcher employed mainly a qualitative research design and data was collected through questionnaires and interviews. Quantitative study was done on officials and responses were ranked according to the importance of the factors influencing community participation. The findings of this research indicate that the community is willing to participate in waste minimization initiatives. 42% of respondents are currently NOT participating in waste minimization initiatives. It was noted that all community respondents promised and are willing to participate in waste minimization strategies. The research further indicates that there is a considerable number of constraints and challenges prohibiting successful community participation in waste minimization. The constraints include lack of knowledge especially regarding composting initiatives to minimize organic waste and the separation of waste, lack of infrastructure to exchange waste for cash, lack of time, lack of transport, lack of political support, lack of starter packs to initiate own waste minimization plant and lack of financial support to ensure that waste minimization initiatives create opportunities for job creation. This study recommends, amongst others, that the community requires support to participate meaningfully in waste minimization initiatives in the form of: awareness and education, infrastructure for reclaimed waste, waste recycling bins, project funding, community involvement and support from the Emfuleni Local Municipality and the private sector. / Thesis (M. Development and Management)--North-West University, Vaal Triangle Campus, 2009.

The purpose of this research is to understand factors contributing to successes and challenges in community participation especially in waste minimization initiatives; in order to inform policies and contribute to improve the design of the initiative. The objectives of the research are: firstly, to understand the current state of public participation in waste minimization at Emfuleni Local Municipality. Secondly, it is to determine the extent of willingness of the community to participate in waste minimization initiatives. Thirdly, to determine strategies on how to promote public participation in waste minimization. Fourthly, to identify constraints and challenges of public participation in waste minimization and what kind of support is required for the community to participate in waste minimization initiatives at Emfuleni Local Municipality. Respondents were drawn from fifty households within the community of Bophelong Extension 13, Emfuleni Recycling groups, Waste Management officials, Gauteng Department of Agriculture, Conservation and Environment stake holder participation unit and Waste Buyers in Vanderbijlpark. The researcher employed mainly a qualitative research design and data was collected through questionnaires and interviews. Quantitative study was done on officials and responses were ranked according to the importance of the factors influencing community participation. The findings of this research indicate that the community is willing to participate in waste minimization initiatives. 42% of respondents are currently NOT participating in waste minimization initiatives. It was noted that all community respondents promised and are willing to participate in waste minimization strategies. The research further indicates that there is a considerable number of constraints and challenges prohibiting successful community participation in waste minimization. The constraints include lack of knowledge especially regarding composting initiatives to minimize organic waste and the separation of waste, lack of infrastructure to exchange waste for cash, lack of time, lack of transport, lack of political support, lack of starter packs to initiate own waste minimization plant and lack of financial support to ensure that waste minimization initiatives create opportunities for job creation. This study recommends, amongst others, that the community requires support to participate meaningfully in waste minimization initiatives in the form of: awareness and education, infrastructure for reclaimed waste, waste recycling bins, project funding, community involvement and support from the Emfuleni Local Municipality and the private sector. / Thesis (M. Development and Management)--North-West University, Vaal Triangle Campus, 2009.

A clique of a graph G is a set of pairwise adjacent nodes of G. Similarly, a biclique (U, V ) of a bipartite graph G is a pair of disjoint, independent vertex sets such that each node in U is adjacent to every node in V in G. We consider the problems of identifying the maximum clique of a graph, known as the maximum clique problem, and identifying the biclique (U, V ) of a bipartite graph that maximizes the product |U | · |V |, known as the maximum edge biclique problem. We show that finding a clique or biclique of a given size in a graph is equivalent to finding a rank one matrix satisfying a particular set of linear constraints. These problems can be formulated as rank minimization problems and relaxed to convex programming by replacing rank with its convex envelope, the nuclear norm. Both problems are NP-hard yet we show that our relaxation is exact in the case that the input graph contains a large clique or biclique plus additional nodes and edges. For each problem, we provide two analyses of when our relaxation is exact. In the first, the diversionary edges are added deterministically by an adversary. In the second, each potential edge is added to the graph independently at random with fixed probability p. In the random case, our bounds match the earlier bounds of Alon, Krivelevich, and Sudakov, as well as Feige and Krauthgamer for the maximum clique problem. We extend these results and techniques to the k-disjoint-clique problem. The maximum node k-disjoint-clique problem is to find a set of k disjoint cliques of a given input graph containing the maximum number of nodes. Given input graph G and nonnegative edge weights w, the maximum mean weight k-disjoint-clique problem seeks to identify the set of k disjoint cliques of G that maximizes the sum of the average weights of the edges, with respect to w, of the complete subgraphs of G induced by the cliques. These problems may be considered as a way to pose the clustering problem. In clustering, one wants to partition a given data set so that the data items in each partition or cluster are similar and the items in different clusters are dissimilar. For the graph G such that the set of nodes represents a given data set and any two nodes are adjacent if and only if the corresponding items are similar, clustering the data into k disjoint clusters is equivalent to partitioning G into k-disjoint cliques. Similarly, given a complete graph with nodes corresponding to a given data set and edge weights indicating similarity between each pair of items, the data may be clustered by solving the maximum mean weight k-disjoint-clique problem. We show that both instances of the k-disjoint-clique problem can be formulated as rank constrained optimization problems and relaxed to semidefinite programs using the nuclear norm relaxation of rank. We also show that when the input instance corresponds to a collection of k disjoint planted cliques plus additional edges and nodes, this semidefinite relaxation is exact for both problems. We provide theoretical bounds that guarantee exactness of our relaxation and provide empirical examples of successful applications of our algorithm to synthetic data sets, as well as data sets from clustering applications.

Thesis (M.Env.St.)--University of Adelaide, Dept. of Geographical and Environmental Studies, 2001. / Author's name appears as Lam Lai Fong Janna on front cover. Bibliography: leaves 117-120.

Thesis (M.E.S.)--Evergreen State College, 1992. / Title from title screen viewed (3/30/2009). "June, 1992." Includes bibliographical references (leaves 39-46).

Thesis (M. Sc.)--University of Hong Kong, 1995. / Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 123-128). Available also in a digital version from Dissertation Abstracts.

Thesis (M.Sc.)--Victoria University of Wellington, 2008. / Includes bibliographical references.