Private Data And Public Value
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Private Data and Public Value
This book investigates the ways in which these systems can promote public value by encouraging the disclosure and reuse of privately-held data in ways that support collective values such as environmental sustainability. Supported by funding from the National Science Foundation, the authors' research team has been working on one such system, designed to enhance consumers ability to access information about the sustainability of the products that they buy and the supply chains that produce them. Pulled by rapidly developing technology and pushed by budget cuts, politicians and public managers are attempting to find ways to increase the public value of their actions. Policymakers are increasingly acknowledging the potential that lies in publicly disclosing more of the data that they hold, as well as incentivizing individuals and organizations to access, use, and combine it in new ways. Due to technological advances which include smarter phones, better ways to track objects and people as they travel, and more efficient data processing, it is now possible to build systems which use shared, transparent data in creative ways. The book adds to the current conversation among academics and practitioners about how to promote public value through data disclosure, focusing particularly on the roles that governments, businesses and non-profit actors can play in this process, making it of interest to both scholars and policy-makers.
Public Value
Author: John Benington
language: en
Publisher: Bloomsbury Publishing
Release Date: 2010-11-30
This text provides a concise and internationalized restatement of the public value approach, an assessment of its impact to date - in theory and practice - and of its particular relevance to the challenges of public management in a time of crisis and austerity.
Privacy-Preserving Data Publishing
Author: Raymond Chi-Wing Wong
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2010-01-01
Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions