Abstract
Local differential privacy (LDP) has received much interest recently. In existing protocols with LDP guarantees, a user encodes and perturbs his data locally before sharing it to the aggregator. In common practice, however, users would prefer not to answer all the questions due to different privacy-preserving preferences for some questions, which leads to data missing or the loss of data quality. In this paper, we demonstrate a new approach for addressing the challenges of data perturbation with consideration of users’ privacy preferences. Specifically, we first propose BiSample: a bidirectional sampling technique value perturbation in the framework of LDP. Then we combine the BiSample mechanism with users’ privacy preferences for missing data perturbation. Theoretical analysis and experiments on a set of datasets confirm the effectiveness of the proposed mechanisms.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (No. 2019QY1402/2016YFB0800901). Jun Zhao’s research was supported by Nanyang Technological University (NTU) Startup Grant M4082311.020, Alibaba-NTU Singapore Joint Research Institute (JRI) M4062640. J4I, Singapore Ministry of Education Academic Research Fund Tier 1 RG128/18, RG115/19, and Tier 2 MOE2019-T2-1-176, and NTU-WASP Joint Project M4082443.020.
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Sun, L., Ye, X., Zhao, J., Lu, C., Yang, M. (2020). BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_6
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