A multi-objective approach to data sharing with privacy constraints and preference based objectives

Public data sharing is utilized in a number of businesses to facilitate the exchange of information. Privacy constraints are usually enforced to prevent unwanted inference of information, specially when the shared data contain sensitive personal attributes. This, however, has an adverse effect on the utility of the data for statistical studies. Thus, a requirement while modifying the data is to minimize the information loss. Existing methods employ the notion of "minimal distortion" where the data is modified only to the extent necessary to satisfy the privacy constraint, thereby asserting that the information loss has been minimized. However, given the subjective nature of information loss, it is often difficult to justify this assertion. In this paper, we propose an evolutionary algorithm to explicitly minimize an achievement function given constraints on the privacy level of the transformed data. Privacy constraints specified in terms of anonymity models are modeled as ad...

• Type: Journal
• Year: 2009
• Where: GECCO
• Authors: Rinku Dewri, Darrell Whitley, Indrajit Ray, Indrakshi Ray
• Keywords: Anonymity Model, GECCO 2009, Information Loss, Optimization, Privacy Constraints