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

Monitoring and recommending privacy settings in social networks

ABSTRACT Ensuring privacy of users of social networks is probably an unsolvable conundrum. It seems, however, that informed use of the existing privacy options by the social network participants may alleviate -or even prevent -some of the more drastic privacyaverse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social network behavior in terms of their privacy settings and broad privacy categories, and 2) recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents early research that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users

Capturing P3P semantics using an enforceable lattice-based structure

With the increasing amount of data collected by service providers, privacy concerns increase for data owners who must provide private data to receive services. Legislative acts require service providers to protect the privacy of customers. Privacy policy frameworks, such as P3P, assist the service providers 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. Furthermore, a privacy-preserving model should consider the privacy preferences of both the data provider and collector. This paper discusses the challenges in development of capturing privacy predicates in a lattice structures. A use case study is presented to show the applicability of the lattice approach to a specific domain. We also present a comprehensive study on applying a lattice-based approach to P3P. We show capturing privacy elements of P3P in a lattice format facilitates managing and enforcing policies presented in P3P and accommodates the customization of privacy practices and preferences of data and service providers. We also propose that the outcome of this approach can be used on lattice-based privacy aware access control models [8]