Abstract
IoT technology collects information from a lot of clients, which may relate to personal privacy. To protect the privacy, the clients would like to encrypt the raw data with their own keys before uploading. However, to make use of the information, the data mining technology with cloud computing is used for the knowledge discovery. Hence, it is an emergent issue of how to effectively performing data mining algorithm on the encrypted data. In this paper, we present a k-means clustering scheme with multi-user based on the IoT data. Although, there are many privacy-preserving k-means clustering protocols, they rarely focus on the situation of encrypting with different public keys. Besides, the existing works are inefficient and impractical. The scheme we propose in this paper not only solves the problem of evaluation on the encrypted data under different public keys but also improves the efficiency of the algorithm. It is semantic security under the semi-honest model according to our theoretical analysis. At last, we evaluate the experiment based on a real dataset, and comparing with previous works, the result shows that our scheme is more efficient and practical.
Highlights
With the growing up of Internet of Things technology, the application of Internet of Things will spread to all walks of life
It is important to note that Stage 1 is run only once whereas Stage 2 and Stage 3 are run in an iterative manner until the termination condition holds
We aim to solve the problems of k-means clustering algorithm on inputs that are encrypted under different independent public keys
Summary
With the growing up of Internet of Things technology, the application of Internet of Things will spread to all walks of life. Hospital wristbands can identify patients undergoing medical care, and sport trackers can log physical activities. All of those smart devices and sensors will produce large amounts of data in its running, and to reduce cost, users would like to benefit from the outsourced services, which is one of the fundamental advantages of cloud computing. The users with resource-constraint devices can delegate the heavy workloads into untrusted cloud servers and enjoy the unlimited computing resources Such a large quantity of data always involves sensitive information, such as medical records or locations information, and it is high risk to store such information directly in the cloud servers. The security challenge is an emergent problem in the cloud computing development
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