Secure two-party k-means clustering
Paul Bunn, Rafail Ostrovsky
Abstract
Paul Bunn, Rafail Ostrovsky
Abstract
The k-Means Clustering problem is one of the most-explored problems in data mining to date. With the advent of protocols that have proven to be successful in performing single database clustering, the focus has shifted in recent years to the question of how to extend the single database protocols to a multiple database setting. To date there have been numerous attempts to create specific multiparty k-means clustering protocols that protect the privacy of each database, but according to the standard cryptographic definitions of "privacy-protection," so far all such attempts have fallen short of providing adequate privacy.
OpenAlex reports 224 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The k-Means Clustering problem is one of the most-explored problems in data mining to date. With the advent of protocols that have proven to be successful in performing single database clustering, the focus has shifted in recent years to the question of how to extend the single database protocols to a multiple database setting. To date there have been numerous attempts to create specific multiparty k-means clustering protocols that protect the privacy of each database, but according to the standard cryptographic definitions of "privacy-protection," so far all such attempts have fallen short of providing adequate privacy.
Key concepts: Cluster analysis, Computer science, Focus (optics), Cryptography, Information privacy, Computer security, Database, Data mining