| Title | Privacy-Preserving Big Data Stream Mining: Opportunities, Challenges, Directions |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Cuzzocrea, Alfredo |
| Conference Name | 2017 IEEE International Conference on Data Mining Workshops (ICDMW) |
| Keywords | Big Data, big data privacy, Data models, data privacy, Human Behavior, human factors, Metrics, mining big data streams, privacy, privacy of big data processing, Privacy-preserving big data stream mining, pubcrawl, Publishing, Resiliency, Scalability, Trajectory |
| Abstract | This paper explores recent achievements and novel challenges of the annoying privacy-preserving big data stream mining problem, which consists in applying mining algorithms to big data streams while ensuring the privacy of data. Recently, the emerging big data analytics context has conferred a new light to this exciting research area. This paper follows the so-depicted research trend. |
| Notes | ISSN: 2375-9259 |
| DOI | 10.1109/ICDMW.2017.140 |
| Citation Key | cuzzocrea_privacy-preserving_2017 |
Privacy-Preserving Big Data Stream Mining: Opportunities, Challenges, Directions