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, Turkey, 16 - 19 May 2016, pp.313-316 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2016.7495740
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.313-316
  • Ankara Yıldırım Beyazıt University Affiliated: Yes


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.