@conference {876,
	title = {Privacy-Preserving Outsourcing of Data Mining},
	booktitle = {40th IEEE Annual Computer Software and Applications Conference, {COMPSAC} Workshops 2016, Atlanta, GA, USA, June 10-14, 2016},
	year = {2016},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = { Atlanta, GA, USA},
	abstract = {Data mining is gaining momentum in society due to the ever increasing availability of large amounts of data, easily gathered by a variety of collection technologies and stored via computer systems. Due to the limited computational resources of data owners and the developments in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service (DMaaS). In this paradigm, a company (data owner) lacking in expertise or computational resources outsources its mining needs to a third party service provider (server). Given the fact that the server may not be fully trusted, one of the main concerns of the DMaaS paradigm is the protection of data privacy. In this paper, we provide an overview of a variety of techniques and approaches that address the privacy issues of the DMaaS paradigm.},
	doi = {10.1109/COMPSAC.2016.169},
	url = {http://dx.doi.org/10.1109/COMPSAC.2016.169},
	author = {Anna Monreale and Hui Wendy Wang}
}
