Recommender Systems for Privacy Management: A Framework

Author

Rasmussen, C. ; Dara, Rozita

Author_Institution

Sch. of Comput. Sci., Univ. of Guelph, Guelph, ON, Canada

fYear

2014

fDate

9-11 Jan. 2014

Firstpage

243

Lastpage

244

Abstract

Social media and online service providers are increasingly collecting personal information. In order for users to make decisions about their online privacy, they will have to read through a dense and hard-to-understand privacy policy. We developed a recommender system to help users make more pertinent decisions with regards to their privacy by providing them with recommendations and warnings based on their privacy preferences. Our ultimate goal is to build intelligent recommender systems that can process any combination of user data and privacy policies to provide recommendations for privacy management to the user.

Keywords

data privacy; recommender systems; hard-to-understand privacy policy; intelligent recommender systems; online privacy; online service providers; personal information collection; privacy management; privacy preferences; social media; Data privacy; Educational institutions; Knowledge based systems; Media; Ontologies; Privacy; Recommender systems; decision making; knowledge base; ontology; privacy; privacy statement; recommender systems;

fLanguage

English

Publisher

ieee

Conference_Titel

High-Assurance Systems Engineering (HASE), 2014 IEEE 15th International Symposium on

Conference_Location

Miami Beach, FL

Print_ISBN

978-1-4799-3465-2

Type

conf

DOI

10.1109/HASE.2014.43

Filename

6754614