基于深度学习的位置大数据统计发布与隐私保护方法

doi:10.11959/j.issn.1000-436x.2022006

Abstract

Abstract:

Aiming at the problems of the unreasonable structure and the low efficiency of the traditional statistical partition and publishing of location big data, a deep learning-based statistical partition structure prediction method and a differential publishing method were proposed to enhance the efficacy of the partition algorithm and improve the availability of the published location big data.Firstly, the two-dimensional space was intelligently partitioned and merged from the bottom to the top to construct a reasonable partition structure.Subsequently, the partition structure matrices were organized as a three-dimensional spatio-temporal sequence, and the spatio-temporal characteristics were extracted via the deep learning model in a bid to realize the prediction of the partition structure.Finally, the differential privacy budget allocation and Laplace noise addition were implemented on the prediction partition structure to realize the privacy protection of the statistical partition and publishing of location big data.Experimental comparison of the real location big data sets proves the advantages of the proposed method in improving the querying accuracy of the published location big data and the execution efficiency of the publishing algorithm.

Key words: privacy protection data publishing, location privacy, private spatial decomposition, differential privacy, deep learning

CLC Number:

TP311

Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG. Statistics release and privacy protection method of location big data based on deep learning[J]. Journal on Communications, 2022, 43(1): 203-216.

Figures/Tables 15

References 39

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层号	各层模型	输出结构	参数量
1	LSTM	(None, 1,32)	528 512
2	LSTM	(None,50)	16 600
3	Dense	(None,4096)	208 896
合计			754 008

层号	各层模型	输出结构	参数量
1	Conv2D	(None,64,64,32)	320
2	MaxPooling2D	(None,32,32,32)	0
3	Conv2D	(None,32,32,64)	18 496
4	MaxPooling2D	(None,16,16,64)	0
5	Conv2D	(None,16,16,64)	36 928
6	Flatten	(None,16384)	0
7	Dense	(None,4096)	67 112 960
合计			67 168 704

层号	各层模型	输出结构	参数量
1	ConvLSTM2D	(None,None,64,64,30)	33 600
2	BatchNormalization	(None,None,64,64,30)	120
3	ConvLSTM2D	(None,None,64,64,30)	64 920
4	BatchNormalization	(None,None,64,64,30)	120
5	Conv3D	(None,None,64,64,1)	811
合计			99 571

层号	各层模型	输出结构	参数量
1	STLSTM2D	(None1,64,64,30)	76 800
2	Reshape	(None,64,64,30)	0
3	Conv2D	(None,64,64,1)	31
4	Reshape	(None,1,64,64,1)	0
合计			76 831

数据集	模型	MSE	RMSE	MAE	宏平均	微平均
	LSTM	0.032 2	0.179 5	0.091 1	0.975 9	0.976 6
BikeShare	CNN	0.329 1	0.573 7	0.244 9	0.855 5	0.859 4
	ConvLSTM	0.001 5	0.038 4	0.023 3	0.925 8	0.933 6
	ST-LSTM	0.027 4	0.165 5	0.135 7	0.988 1	0.988 3
	LSTM	0.153 3	0.391 5	0.276 6	0.608 2	0.718 8
Macquarie Park	CNN	0.308 7	0.555 6	0.283 8	0.880 7	0.859 4
	ConvLSTM	0.038 2	0.195 6	0.081 1	0.934 7	0.937 5
	ST-LSTM	0.046 9	0.216 5	0.046 9	0.947 5	0.953 1
	LSTM	0.250 4	0.490 0	0.331 5	0.662 7	0.790 1
Yellow_tripdata	CNN	0.175 7	0.402 8	0.234 3	0.725 5	0.853 2
	ConvLSTM	0.317 3	0.548 3	0.338 7	0.647 6	0.783 7
	ST-LSTM	0.089 6	0.298 8	0.230 9	0.801 0	0.911 5

Statistics release and privacy protection method of location big data based on deep learning

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References 39

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参数	数据集
参数	BikeShare	Macquarie Park	Yellow_tripdata
覆盖范围	6.59 km×7.13 km	2.43 km×2.72 km	110.59 km×110.13 km
数据规模	70万	340万	1 400万
q₁	0.11 km×0.11km	0.04 km×0.04 km	0.44 km×0.44 km
q₂	0.22 km×0.22 km	0.08 km×0.08 km	0.88 km×0.88 km
q₃	0.44 km×0.44 km	0.16 km×0.16 km	1.76 km×1.76 km
q₄	0.88 km×0.88 km	0.32 km×0.32 km	3.52 km×3.52 km
q₅	1.76 km×1.76 km	0.64 km×0.64 km	7.04 km×7.04 km
q₆	3.52 km×3.52 km	1.28 km×1.28 km	14.08 km×14.08 km

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