Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity
- PDF / 1,382,557 Bytes
- 27 Pages / 439.37 x 666.142 pts Page_size
- 39 Downloads / 191 Views
Sensitive attribute privacy preservation of trajectory data publishing based on l‑diversity Lin Yao1,2 · Zhenyu Chen3 · Haibo Hu4 · Guowei Wu5 · Bin Wu6 Accepted: 5 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form often contain sensitive attributes and publishing such data will leak individuals’ privacy. Especially, a privacy threat occurs when an attacker can link a record to a specific individual based on some known partial information. Therefore, maintaining privacy in the published data is a critical problem. To prevent record linkage, attribute linkage, and similarity attacks based on the background knowledge of trajectory data, we propose a data privacy preservation with enhanced l-diversity. First, we determine those critical spatial-temporal sequences which are more likely to cause privacy leakage. Then, we perturb these sequences by adding or deleting some spatial-temporal points while ensuring the published data satisfy our ( L, 𝛼, 𝛽)-privacy, an enhanced privacy model from l-diversity. Our experiments on both synthetic and real-life datasets suggest that our proposed scheme can achieve better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory. Keywords Sensitive attribute · Privacy preservation · Trajectory data publishing
1 Introduction The popularity of smart mobile devices with positioning technologies triggers the collection of location information by suppliers, corporations, individuals etc. for knowledge-based decision making. Therefore, vast amounts of trajectory data are collected with other information. Data miners have also shown great interest in analyzing these data to provide plentiful serves for people. For example, recent studies [1, 2] have shown that tracking the environmental exposure of a person with his daily trajectories helps to improve diagnose. Therefore, wearable devices * Guowei Wu [email protected] Extended author information available on the last page of the article
13
Vol.:(0123456789)
Table 1 Original table
Table 2 Table without explicit identifier
Distributed and Parallel Databases ID.
Name
Trajectory
Disease
⋅⋅⋅
1
Alice
a1 → d2 → b3 → e4 → f 6 → e8
HIV
⋅⋅⋅
2
Bob
d2 → c5 → f 6 → c7 → e9
Flu
⋅⋅⋅
3
Caesar
b3 → f 6 → c7 → e8
SARS
⋅⋅⋅
4
Daniel
b3 → e4 → f 6 → e8
Fever
⋅⋅⋅
5
Eden
a1 → d2 → c5 → f 6 → c7
Flu
⋅⋅⋅
6
Freeman
c5 → f 6 → e9
SARS
⋅⋅⋅
7
Georgia
f 6 → c7 → e8
Fever
⋅⋅⋅
8
Hugo
a1 → c2 → b3 → c7 → e9
SARS
⋅⋅⋅
9
Ishtar
e4 → f 6 → e8
Fever
⋅⋅⋅
Trajectory
Disease
a1 → d2 → b3 → e4 → f 6 → e8
HIV
d2 → c5 → f 6 → c7 → e9
Flu
b3 → f 6 → c7 → e8
SARS
b3 → e4 → f 6 → e8
Fever
a1 → d2 → c5 → f 6 → c7
Flu
c5 → f 6 → e9
SARS
f 6 → c7 → e8
Fever
a1 → c2 → b3 → c7 → e9
SARS
e4 → f 6 → e8
Fever
have been generating tremendous amounts of location-rich,
Data Loading...
