Title

Toward Transparent and Accountable Privacy-Preserving Data Classification

Publication Name

IEEE Network

Abstract

Machine learning provides an effective approach to execute big data analysis. As a branch of machine learning, classification has been widely adopted in data processing. However, the sensitivity of data raises the concern of data privacy. How to balance data utility and data privacy is a challenging issue. Privacy-preserving data classification, which supports flexible and privacy-friendly access to datasets and data classification, enables users' data to be collected in an authenticated manner. However, the priva-cy-preserving data classification approach has a limitation in that the correctness of data classification cannot be guaranteed. As a consequence, it is possible for a malicious classifier to manipulate the classification result. To solve these problems, in this article, we propose a transparent and accountable privacy-preserving data classification framework, which involves a tracer to assert the behavior of the classifier and maintains the utility and privacy of data. Specifically, we take advantage of cryptography techniques to balance data privacy and data utility, and use blockchain to achieve transparency and accountability for the behavior of the classifier. To illustrate the practicability of this framework, we implement concrete cryptographic algorithms and develop a prototype system to evaluate and test its performance.

Open Access Status

This publication is not available as open access

Volume

35

Issue

4

Article Number

9406311

First Page

184

Last Page

189

Funding Number

61872229

Funding Sponsor

National Natural Science Foundation of China

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/MNET.011.2000662