Abstract
Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.
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Minnesota Population Center. Integrated public use microdata series-international: Version 5.0. 2009. https://international.ipums.org.
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Acknowledgement
This research was supported by the Patient-Centered Outcomes Research Institute (PCORI) under contract ME-1310-07058, the National Institute of Health (NIH) under award number R01GM114612, R01GM118609, and the National Science Foundation under award CNS-1618932.
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Li, H., Xiong, L., Ji, Z., Jiang, X. (2017). Partitioning-Based Mechanisms Under Personalized Differential Privacy. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_48
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DOI: https://doi.org/10.1007/978-3-319-57454-7_48
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