Group relational privacy protection on time-constrained point of interests

doi:10.1007/s11704-022-2090-9

Front. Comput. Sci.

2023, Vol. 17

Issue (3) : 173607 https://doi.org/10.1007/s11704-022-2090-9

LETTER

Group relational privacy protection on time-constrained point of interests

Bo NING¹(

), Xiaonan LI¹, Fan YANG¹, Yunhao SUN², Guanyu LI¹, George Y. YUAN³

¹. Faculty of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
². Faculty of Software, Dalian University of Foreign Languages, Dalian 116044, China
³. Thinvent Digital Technology Co., Ltd., Nanchang 330029, China

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Corresponding Author(s): Bo NING

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 08 June 2022 Issue Date: 16 September 2022

Cite this article:

Bo NING,Xiaonan LI,Fan YANG, et al. Group relational privacy protection on time-constrained point of interests[J]. Front. Comput. Sci., 2023, 17(3): 173607.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-2090-9
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I3/173607

$U$	$T$
$U$	$t 1$	$t 2$	$t 3$	$t 4$	$t 5$	$t 6$
$u 1$	a	a	b	b	c	e
$u 2$	a	a	c	b	c	f
$u 3$	d	d	c	b	e	f
$u 4$	d	d	b	c	c	f
$u 5$	d	d	c	c	e	e

Tab.1 Time-constrained data set of user PoIs

Tab.2 Distance matrix of PoIs

Fig.1 Experiments of CARC, ERQ, average CA(ACA) and number of changing PoIs. (a) Dataset of smart driving; (b) dataset of Shanghai PoIs

Dataset	$m$	$n$	$k$	$\|$ PoIs $\|$	Partition
Smart driving	3	2	3	${9, 18, 50}$	$T$ = $96$
Shanghai PoI	6	4	3	${50, 100, 150}$	$p$ = $156$

Tab.3 Parameter settings

1	Y, Yuan G, Wang J Y, Xu L Chen . Efficient distributed subgraph similarity matching. The VLDB Journal, 2015, 24( 3): 369– 394
2	Y F, Ge J, Cao H, Wang Z, Chen Y Zhang . Set-based adaptive distributed differential evolution for anonymity-driven database fragmentation. Data Science and Engineering, 2021, 6( 4): 380– 391
3	J, Xu J, Zhao R, Zhou C, Liu P, Zhao L Zhao . Predicting destinations by a deep learning based approach. IEEE Transactions on Knowledge and Data Engineering, 2021, 33( 2): 651– 666
4	T, Cai J, Li A, Mian R H, Li T, Sellis J X Yu . Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering, 2022, 34( 4): 1993– 2007 https://doi.org/10.1109/TKDE.2020.3003047
5	Y, Tong Y, Zeng B, Ding L, Wang L Chen . Two-sided online micro-task assignment in spatial crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, 2021, 33( 5): 2295– 2309
6	X, Xiao Y Tao. Anatomy: simple and effective privacy preservation. In: Proceedings of the 32nd International Conference on Very Large Data Bases. 2006, 139– 150

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