62,023 research outputs found
Knowledge data discovery and data mining in a design environment
Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development
Privacy Games: Optimal User-Centric Data Obfuscation
In this paper, we design user-centric obfuscation mechanisms that impose the
minimum utility loss for guaranteeing user's privacy. We optimize utility
subject to a joint guarantee of differential privacy (indistinguishability) and
distortion privacy (inference error). This double shield of protection limits
the information leakage through obfuscation mechanism as well as the posterior
inference. We show that the privacy achieved through joint
differential-distortion mechanisms against optimal attacks is as large as the
maximum privacy that can be achieved by either of these mechanisms separately.
Their utility cost is also not larger than what either of the differential or
distortion mechanisms imposes. We model the optimization problem as a
leader-follower game between the designer of obfuscation mechanism and the
potential adversary, and design adaptive mechanisms that anticipate and protect
against optimal inference algorithms. Thus, the obfuscation mechanism is
optimal against any inference algorithm
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.Comment: Accepted at ICDE 2019. Please cite the ICDE 2019 proceedings versio
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
- …