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Authors: Zhang, Jun | He, Meiqi | Zeng, Gongxian | Yiu, Siu-Ming

Article Type: Research Article

Abstract: With the popularity of cloud computing, an increasing number of institutions outsource their data to a third-party cloud system which could be untrusted. The institutions encrypt their data before outsourcing to protect data privacy. On the other hand, data mining techniques are used widely but computationally intensive, especially for large datasets. Combining data from different institutions for a big and varied training set helps enhance data mining performance. Therefore, it is important to make the cloud system which has powerful computing abilities run data mining algorithms on the encrypted data from multiple institutions. Two challenges need attention – how to …compute on encrypted data under multiple keys and how to verify the correctness of the result. There are no existing methods that solve the two challenges at the same time. Elastic net is a useful linear regression tool to find genomic biomarkers. In this paper, we propose the first privacy-preserving verifiable elastic net protocol based on reduction to support vector machine using two non-colluding servers. We construct a homomorphic cryptosystem that supports one multiply operation and multiple add operations under both single key and different keys. We allow the involved institutions to verify the correctness of the final result. The collaboration between multiple institutions is made possible without jeopardizing the privacy of data records. We formally prove that our protocol is secure and implement the protocol. The experimental results show that our protocol runs reasonably fast, and thus can be applied in practice. Show more

Keywords: Privacy-preserving elastic net, support vector machine, verifiable computation, multiple encryption keys

DOI: 10.3233/JCS-171107

Citation: Journal of Computer Security, vol. 26, no. 6, pp. 791-815, 2018