Personalized privacy in open data sharing scenarios

Abstract

Purpose The purpose of this paper is to propose a privacy-preserving paradigm for open data sharing based on the following foundations: subjects have unique privacy requirements; personal data are usually published incrementally in different sources; and privacy has a time-dependent element. Design/methodology/approach This study first discusses the privacy threats related to open data sharing. Next, these threats are tackled by proposing a new privacy-preserving paradigm. The main challenges related to the enforcement of the paradigm are discussed, and some suitable solutions are identified. Findings Classic privacy-preserving mechanisms are ineffective against observers constantly monitoring and aggregating pieces of personal data released through the internet. Moreover, these methods do not consider individual privacy needs. Research limitations/implications This study characterizes the challenges to the tackled by a new paradigm and identifies some promising works, but further research proposing specific technical solutions is suggested. Practical implications This work provides a natural solution to dynamic and heterogeneous open data sharing scenarios that require user-controlled personalized privacy protection. Social implications There is an increasing social understanding of the privacy threats that the uncontrolled collection and exploitation of personal data may produce. The new paradigm allows subjects to be aware of the risks inherent to their data and to control their release. Originality/value Contrary to classic data protection mechanisms, the new proposal centers privacy protection on the individuals, and considers the privacy risks through the whole life cycle of the data release.