Chapter title |
Partitioning-Based Mechanisms Under Personalized Differential Privacy
|
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Chapter number | 48 |
Book title |
Advances in Knowledge Discovery and Data Mining
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Published in |
Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings. Part I. Pacific-Asia Conference on Knowledge Discovery and Data Mining (21st : 2017 : Cheju Isl..., May 2017
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DOI | 10.1007/978-3-319-57454-7_48 |
Pubmed ID | |
Book ISBNs |
978-3-31-957453-0, 978-3-31-957454-7
|
Authors |
Haoran Li, Li Xiong, Zhanglong Ji, Xiaoqian Jiang |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 7 | 41% |
Researcher | 4 | 24% |
Student > Doctoral Student | 1 | 6% |
Lecturer | 1 | 6% |
Student > Master | 1 | 6% |
Other | 1 | 6% |
Unknown | 2 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 24% |
Engineering | 4 | 24% |
Social Sciences | 3 | 18% |
Unknown | 6 | 35% |