Proportional Differential Privacy Budget Allocation Method for Partition and Publishing of Statistical Big Data

doi:10.3969/j.issn.1671-1122.2022.11.004

Abstract

Abstract:

In view of the problem of privacy budget allocation method for the existing big data differential privacy statistical partition and publishing, this paper proposed a proportional differential privacy budget allocation method. The hierarchical allocation formula of the proportional privacy budget allocation method was derived through the analysis of the statistical partitioning structure and publishing error of big data. The proposed method was compared with other existing privacy budget allocation methods to prove its advantages theoretically in terms of privacy budget allocation results for each partition layer and the overall publishing error. The experimental results show that the proposed proportional differential privacy budget allocation method has better range counting query accuracy than other existing privacy budget allocation methods, which is helpful to improve the availability of big data statistical partitioning and publishing results.

Key words: privacy preserving data publishing, privacy spatial decomposition, differential privacy, privacy budget allocation

CLC Number:

TP309

YAN Yan, ZHANG Xiong, FENG Tao. Proportional Differential Privacy Budget Allocation Method for Partition and Publishing of Statistical Big Data[J]. Netinfo Security, 2022, 22(11): 24-35.

Figures/Tables 11

References 34

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参数	数据集
参数	Landmark	Checkin	Yellow_tripdata
数据总量	870052	1000000	12504938
覆盖范围/km²	6634×4423	39806×16586	110×110
最小查询范围/km²	1×1	10×10	0.5×0.5
中等查询范围/km²	10×10	100×100	5×5
最大查询范围/km²	100×100	1000×1000	50×50