A differential privacy protecting K-means clustering algorithm based on contour coefficients
By Yaling Zhang, Na Liu, Shangping Wang. 2018
56 citations recorded by the source corpus.
This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time.
Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Eugenio Martínez-Cámara
- M. V. Luzón
- Daniel Jiménez-López
- José Antonio Ruiz-Millán
- Gerardo González-Seco
- G. Stipcich
- Francisco Herrera
- M. Veganzones
Citations in the corpus
- Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, background): “Although not in a FL context, in Zhang et al. [38] the authors adjust the Laplace noise added to each centroid based on the contour coefficients.”