2018
DOI: 10.1016/j.ins.2018.02.056
|View full text |Cite
|
Sign up to set email alerts
|

Differentially private Naive Bayes learning over multiple data sources

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
70
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 183 publications
(70 citation statements)
references
References 30 publications
0
70
0
Order By: Relevance
“…In the method proposed in this paper, if using scheme (a) and scheme (b) in Section 5.1, the response time is the same as that of the whole network model and can significantly reduce the storage capacity. If using scheme (c), the storage capacity can be further reduced, but the security depends on the size of each BCS and the verification sampling rate [39]. The greater the number of BCS nodes and the larger the sampling rate, the greater the credibility.…”
Section: Efficiency Analysismentioning
confidence: 99%
“…In the method proposed in this paper, if using scheme (a) and scheme (b) in Section 5.1, the response time is the same as that of the whole network model and can significantly reduce the storage capacity. If using scheme (c), the storage capacity can be further reduced, but the security depends on the size of each BCS and the verification sampling rate [39]. The greater the number of BCS nodes and the larger the sampling rate, the greater the credibility.…”
Section: Efficiency Analysismentioning
confidence: 99%
“…Data owners often suppress their data for an untrusted trainer to train a classifier due to privacy concerns. Li et al proposed a privacy preserving solution for learning algorithms based on differentially private naive Bayes learning, allowing a trainer to build a classifier over the data from a single owner [37]. Data privacy also becomes a central consideration in vehicular networks, where outsourcing data to the cloud server is done [38].…”
Section: Vanet Security Requirementsmentioning
confidence: 99%
“…At present, IoT has been widely used in many fields, like food safety, smart health (s-health), urban construction, cloud storage, etc. [1][2][3][4].…”
Section: Introductionmentioning
confidence: 99%