Abstract

Abstract- The collection of digital information by governments,corporations and individuals has created tremendous opportunities for knowledgeand information-based decision making. Driven by mutual benefits, or byregulations that require certain data to be published, there is a demand forthe exchange and publication of data among various parties. Data in itsoriginal form, however, typically contains sensitive information aboutindividuals, and publishing such data will violate individual privacy. Thecurrent practice in data publishing relies mainly on policies and guidelines asto what type of data can be published and on agreements on the use of publisheddata. This approach alone may lead to excessive data distortion or insufficientprotection. Privacy-preserving data publishing (PPDP) provides methods andtools for publishing useful information while preserving data privacy.Recently, PPDP has received considerable attention in research communities, andmany approaches have been proposed for different data publishing scenarios. Inthis survey, we will systematically summarize and evaluate different approachesto PPDP, study the challenges in practical data publishing, clarify thedifferences and requirements that distinguish PPDP from other related problems,and propose future research directions.    

Keywords—depth-tracing, privacy preservation,publishing, information- based decision making, data market.