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DRAPE: optimizing private data release under adjustable privacy-utility equilibrium

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Abstract

Data releasing and sharing between several fields has became inevitable tendency in the context of big data. Unfortunately, this situation has clearly caused enormous exposure of sensitive and private information. Along with massive privacy breaches, privacy-preservation issues were brought into sharp focus and privacy concerns may prevent people from providing their personal data. To meet the requirements of privacy protection, such a problem has been extensively studied. However, privacy protection of sensitive information should not prevent data users from conducting valid analyses of the released data. We propose a novel algorithm in this paper, named Data Release under Adjustable Privacy-utility Equilibrium (DRAPE), to address this problem. We handle the privacy versus utility tradeoff in the data release problem by breaking sensitive associations among variables while maintaining the correlations of nonsensitive variables. Furthermore, we quantify the impact of the proposed privacy-preserving method in terms of correlation preservation and privacy level, and thereby develop an optimization model to fulfil data privacy and data utility constraints. The proposed approach is not only able to provide a better privacy levels control scheme for data publishers, but also provides personalized service for data requesters with different utility requirements. We conduct experiments on one simulated dataset and two real datasets, and the simulation results show that DRAPE efficiently achieves a guaranteed privacy level while simultaneously effectively preserving data utility.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 71871090), the Science & Technology Innovation Leading Project of Hunan High-tech Industry (No. 2020GK2005) and the Natural Science Foundation of Hunan Province of China (No. 2021JJ30158).

Funding

This work was supported by the National Natural Science Foundation of China (No. 71871090), the Science & Technology Innovation Leading Project of Hunan High-tech Industry (No. 2020GK2005) and the Natural Science Foundation of Hunan Province of China (No. 2021JJ30158).

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Authors and Affiliations

  1. School of Business, Hunan University, Changsha, 410082, China

    Qingyue Xiong, Qiujun Lan, Jiaqi Ma & Huiling Zhou

  2. School of Information Technology, Deakin University, Geelong, VIC, 3216, Australia

    Gang Li

  3. Tianheguoyun Technology Co., Ltd., Hunan Tianhe Blockchain Research Institute, Changsha, 410019, China

    Zheng Yang

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  1. Qingyue Xiong
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  2. Qiujun Lan
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  4. Huiling Zhou
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  5. Gang Li
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Correspondence to Qiujun Lan.

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Xiong, Q., Lan, Q., Ma, J. et al. DRAPE: optimizing private data release under adjustable privacy-utility equilibrium. Inf Technol Manag 25, 199–217 (2024). https://doi.org/10.1007/s10799-022-00378-4

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