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Privacy concerns exist whenever sensitive data relating to people is collected. Finding a way to preserve and guarantee an individual's privacy has always been of high importance. Some may decide not to reveal their data to protect their privacy. It has become impossible to take advantage of many essential customized services without disclosing any identifying or sensitive data. The challenge is that each data item may have a different value for different individuals. These values can be defined by applying weights that describe the importance of data items for individuals if that particular private data item is exposed. We propose a generic framework to capture these weights from data providers, which can be considered as a mediator to quantify privacy compromisation. This framework also helps us to identify what portion of a targeted population is vulnerable to compromise their privacy in return for receiving certain incentives. Conversely, the model could assist researchers to offer appropriate incentives to a targeted population to facilitate collecting useful data. [ABSTRACT FROM AUTHOR]

Purpose is a key concept in privacy policies. Although some models have been proposed for enforcing purposebased privacy policies, little has been done in defining formal semantics for purpose, and therefore an effective enforcement mechanism for such policies has remained a challenge. We have developed a framework for expressing and enforcing such policies by giving a formal definition of purpose and proposing a modal-logic language for formally expressing purpose constraints. The semantics of this language are defined over an abstract model of workflows. Based on this formal framework, we discuss some properties of purpose, show how common forms of purpose constraints can be formalized, how purpose-based constraints can be connected to more general access control policies, and how they can be enforced in a workflow-based information system by extending common access control technologies. [ABSTRACT FROM AUTHOR]