|
|
|
Geo-social network publication based on differential privacy |
Xiaochun WANG, Yidong LI( ) |
| School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China |
|
|
|
|
|
|
Corresponding Author(s):
Yidong LI
|
|
Online First Date: 15 November 2018
Issue Date: 04 December 2018
|
|
| 1 |
Xiong P, Zhu T, Niu W, Li G. A differentially private algorithm for location data release. Knowledge and Information Systems, 2016, 47(3): 647–669
https://doi.org/10.1007/s10115-015-0856-1
|
| 2 |
Albelaihy A, Cazalas J. A survey of the current trends of privacy techniques employed in protecting the location privacy of users in LBSs. In: Proceedings of the 2nd International Conference on Anti-Cyber Crimes. 2017, 19–24
https://doi.org/10.1109/Anti-Cybercrime.2017.7905256
|
| 3 |
Li J, Liu Z, Chen X, Xhafa F, Tan X, Wong D S. L-EncDB: a lightweight framework for privacy-preserving data queries in cloud computing. Knowledge-Based Systems, 2015, 79: 18–26
https://doi.org/10.1016/j.knosys.2014.04.010
|
| 4 |
Li P, Li J, Huang Z, Li T, Gao C Z, Yiu S M, Chen K. Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems, 2017, 74: 76–85
https://doi.org/10.1016/j.future.2017.02.006
|
| 5 |
Gao C, Cheng Q, He P, Susilo W, Li J. Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences, 2018, 444: 72–88
https://doi.org/10.1016/j.ins.2018.02.058
|
| 6 |
Li Y C, Li Y D, Xu G. Protecting private geosocial networks against practical hybrid attacks with heterogeneous information. Neurocomputing, 2016, 210: 81–90
https://doi.org/10.1016/j.neucom.2015.08.132
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
