| Title | Privacy Preserving Big Data Publishing |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Canbay, Yavuz, Vural, Yilmaz, Sagiroglu, Seref |
| Conference Name | 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) |
| Keywords | anonymization, Big Data, big data privacy, big data publishing, cyber terrorism, Data models, data privacy, Deep Learning, Human Behavior, human factors, Metrics, privacy, Privacy-preserving, pubcrawl, Publishing, Resiliency, Scalability |
| Abstract | In order to gain more benefits from big data, they must be shared, published, analyzed and processed without having any harm or facing any violation and finally get better values from these analytics. The literature reports that this analytics brings an issue of privacy violations. This issue is also protected by law and bring fines to the companies, institutions or individuals. As a result, data collectors avoid to publish or share their big data due to these concerns. In order to obtain plausible solutions, there are a number of techniques to reduce privacy risks and to enable publishing big data while preserving privacy at the same time. These are known as privacy-preserving big data publishing (PPBDP) models. This study presents the privacy problem in big data, evaluates big data components from privacy perspective, privacy risks and protection methods in big data publishing, and reviews existing privacy-preserving big data publishing approaches and anonymization methods in literature. The results were finally evaluated and discussed, and new suggestions were presented. |
| DOI | 10.1109/IBIGDELFT.2018.8625358 |
| Citation Key | canbay_privacy_2018 |
Privacy Preserving Big Data Publishing