Abstract
Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.
Highlights
At present, smart cities include more innumerable superior Internet of Things (IoT) infrastructures [1] to manage their component efficiently [2]
To handle the above challenges, we propose secure K-Nearest Neighbour (K-NN), a privacy-preserving K-NN schema based on Blockchain and encrypted data of IoT devices
T p + tn + f p + f n tp tp + f p tp t p + fn where t p is the number of relevant that are labeled precisely, f p is the numbers of irrelevant that are labeled correctly, f n is the numbers of relevant that are mislabeled and tn is the number of irrelevant that are mislabeled in the test outcomes
Summary
Smart cities include more innumerable superior IoT infrastructures [1] to manage their component efficiently [2]. K-means [5] and K-NN [6] are pre-eminent unsupervised and supervised learning models, respectively, that can effectively implement data classification amid all ML models [7] These ML models have been used in various specialties to answer real-world classification dilemmas in IoT-enabled smart health. Let the two-party probabilistic polynomial time functionalities be F1 , F2 , ..., Fn and F1 , F2 , ..., Fn is calculated by the protocols ρ1 , ρ2 , ..., ρn in the presence of curious-but-honest adversaries. Let, two-party probabilistic polynomial time functionality be G and G is securely computed in the F1 , F2 , ..., Fn by a protocol π - hybrid model in the presence of curious-but-honest adversaries. Π ρ1 ,ρ2 ,...,ρn securely calculate G in the presence of curious-but-honest adversaries
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