Abstract
The challenge in privacy-preserving data mining is avoiding the invasion of personal data privacy. Secure computa- tion provides a solution to this problem. With the development of this technique, fully homomorphic encryption has been realized after decades of research; this encryption enables the computing and obtaining results via encrypted data without accessing any plaintext or private key information. In this paper, we propose a privacy-preserving clustering using representatives (CURE) algorithm over arbitrarily partitioned data using fully homomor- phic encryption. Our privacy-preserving CURE algorithm allows cooperative computation without revealing users' individual data. The method used in our algorithm enables the data to be arbitrarily distributed among different parties and to receive accurate clustering result simultaneously.
Highlights
With the advent of the Big Data Era, people are beginning to care more about the security of their private data
RSA is a multiplicatively homomorphic encryption scheme that can efficiently compute a ciphertext that encrypts the product of the original plaintext
Since Gentry developed the first fully homomorphic encryption scheme, many researchers have tried to provide privacy-preserving applications based on the FHE
Summary
With the advent of the Big Data Era, people are beginning to care more about the security of their private data. RSA is a multiplicatively homomorphic encryption scheme that can efficiently compute a ciphertext that encrypts the product of the original plaintext Based on this privacy homomorphism method, people are realizing that they can store encrypted data instead of plaintext on the cloud. We propose a privacy-preserving solution to a simple clustering algorithm (“CU RE”) over arbitrarily partitioned data. What happens if the two companies are not willing to share the real data with each other To solve this problem, we propose a privacy-preserving method for clustering using representatives. To the best of our knowledge, we are the first to study a privacy protection in clustering algorithm using fully homomorphic encryption.
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