| Title | Privacy Preserving Multiclass Classification for Horizontally Distributed Data |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Lu, Yunmei, Yan, Mingyuan, Han, Meng, Zhang, Qingliang, Zhang, Yanqing |
| Conference Name | Proceedings of the 19th Annual SIG Conference on Information Technology Education |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5954-2 |
| Keywords | composability, Metrics, multiclass classification, Privacy preserve, pubcrawl, Resiliency, support vector machine, Support vector machines |
| Abstract | With the advent of the era of big data, applying data mining techniques on assembling data from multiple parties (or sources) has become a leading trend. In this work, a Privacy Preserving Multiclass Classification (PPM2C) method is proposed. Experimental results show that PPM2C is workable and stable. |
| URL | http://doi.acm.org/10.1145/3241815.3241889 |
| DOI | 10.1145/3241815.3241889 |
| Citation Key | lu_privacy_2018 |
Privacy Preserving Multiclass Classification for Horizontally Distributed Data