Privacy preserving extreme learning machine classification model for distributed systems
| Autor: | Ahmet Fatih Mustacoglu, Ahmet E. Topcu, Ferhat Ozgur Catak |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Information privacy business.industry Computer science Active learning (machine learning) Big data Stability (learning theory) Online machine learning 02 engineering and technology computer.software_genre Machine learning 020901 industrial engineering & automation Computational learning theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence Instance-based learning business computer Extreme learning machine |
| Zdroj: | SIU |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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