A Model for Privacy Compromisation Value

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.