Efficient Privacy Preserving Distributed K-Means for Non-IID Data

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Brandao, A (Author) Other; Mendes, R (Author) Other; João P. Vilela (Author) FCUP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XIX, IDA 2021

Pages: 439-451

19th International Symposium on Intelligent Data Analysis (IDA)

ELECTR NETWORK, APR 26-28, 2021

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00T-YV0

DOI: 10.1007/978-3-030-74251-5_35

Abstract (EN): Privacy is becoming a crucial requirement in many machine learning systems. In this paper we introduce an efficient and secure distributed K-Means algorithm, that is robust to non-IID data. The base idea of our proposal consists in each client computing the K-Means algorithm locally, with a variable number of clusters. The server will use the resultant centroids to apply the K-Means algorithm again, discovering the global centroids. To maintain the client's privacy, homomorphic encryption and secure aggregation is used in the process of learning the global centroids. This algorithm is efficient and reduces transmission costs, since only the local centroids are used to find the global centroids. In our experimental evaluation, we demonstrate that our strategy achieves a similar performance to the centralized version even in cases where the data follows an extreme non-IID form.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13