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http://dx.doi.org/10.6109/jkiice.2021.25.12.1904

Performance Analysis for Privacy-preserving Data Collection Protocols  

Lee, Jongdeog (Department of Computer Science, Korea Military Academy)
Jeong, Myoungin (Department of Mathematics, Korea Military Academy)
Yoo, Jincheol (Department of Computer Science, Korea Military Academy)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1904-1913 More about this Journal
Abstract
With the proliferation of smart phones and the development of IoT technology, it has become possible to collect personal data for public purposes. However, users are afraid of voluntarily providing their private data due to privacy issues. To remedy this problem, mainly three techniques have been studied: data disturbance, traditional encryption, and homomorphic encryption. In this work, we perform simulations to compare them in terms of accuracy, message length, and computation delay. Experiment results show that the data disturbance method is fast and inaccurate while the traditional encryption method is accurate and slow. Similar to traditional encryption algorithms, the homomorphic encryption algorithm is relatively effective in privacy preserving because it allows computing encrypted data without decryption, but it requires high computation costs as well. However, its main cost, arithmetic operations, can be processed in parallel. Also, data analysis using the homomorphic encryption needs to do decryption only once at any number of data.
Keywords
Homomorphic encryption; Privacy; Data collection protocol; Internet of Things;
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