Privacy Preserving Extreme Learning Machine Classification Model for Distributed Systems

Catak F. O. , Mustacoglu A. F. , TOPCU A. E.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 16 - 19 Mayıs 2016, ss.313-316 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu.2016.7495740
  • Basıldığı Şehir: Zonguldak
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.313-316


Machine learning based classification methods are widely used to analyze large scale datasets in this age of big data. Extreme learning machine (ELM) classification algorithm is a relatively new method based on generalized single-layer feed-forward network structure. Traditional ELM learning algorithm implicitly assumes complete access to whole data set. This is a major privacy concern in most of cases. Sharing of private data (i.e. medical records) is prevented because of security concerns. In this research, we proposed an efficient and secure privacy-preserving learning algorithm for ELM classification over data that is vertically partitioned among several parties. The new learning method preserves the privacy on numerical attributes, builds a classification model without sharing private data without disclosing the data of each party to others.