A Model for Privacy Compromisation Value

AbstractPrivacy 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

A Privacy Preserving Model Bridging Data Provider and Collector Preferences

With the increasing amount of data collected by service providers, privacy concerns increase for data owners who provide private data to receive services. Legislative acts require service providers to protect the privacy of customers. Privacy policy frameworks, such as P3P, assist them by describing their privacy policies to customers (e.g. publishing privacy policy on websites). Unfortunately, providing the policies alone does not guarantee that they are actually enforced because privacy is not a key feature of conventional access control models. A privacy-preserving model should consider the privacy preferences of both the data provider and collector. This paper briefly develops a Lattice-based Privacy Aware Access Control (LPAAC) Model that enforces privacy policies, facilitates customization of privacy agreements, and accommodates preferences of both data and service providers. We demonstrate our model’s design and feasibility with corresponding privacy catalogues. Examples clarify the usability, and we provide the implementation of our privacy catalogues that show the efficiency and scalability of our model