A novel subgraph $$K^{+}$$ K + -isomorphism method in social network based on graph similarity detection

Rong, Huan; Ma, Tinghuai; Tang, Meili; Cao, Jie

doi:10.1007/s00500-017-2513-y

Cited by 58 publications

(19 citation statements)

References 37 publications

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“…However, such collaboration will raise privacy concerns that are related to patients' records [Alabdulkarim, Al-Rodhaan and Tian (2017); Liang, Lu, Chen et al (2011); Liu, Lu, Ma et al (2016); Lu, Lin and Shen (2013)]. The privacy of shared data is a serious issue [Ma, Zhang, Cao et al (2015); Rong, Ma, Tang et al (2018); Xiong and Shi (2018)]; thus, privacy preserving algorithms for securing data-mining techniques enable the extraction of hidden patterns from a dataset without actually accessing it [Zhang, Tong, Tang et al (2005)]. A privacy-Preserving naïve Bayes classifier (PPNBC) model was proposed in Liu et al [Liu, Lu, Ma et al (2016)].…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Alabdulkarim¹,

Al-Rodhaan²,

Al-Dhelaan³

2019

Computers, Materials &Amp; Continua

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Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust. These systems are used to improve physicians' diagnostic processes in terms of speed and accuracy. Using data-mining techniques, a clinical decision support system builds a classification model from hospital's dataset for diagnosing new patients using their symptoms. In this work, we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients' information and exposing them to cyber and network attacks. Solving the same problem with a different methodology, the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms. Moreover, our model is validated against the privacy requirements of the hospitals' datasets and votes, and patients' diagnosed symptoms.

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Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Alabdulkarim¹,

Al-Rodhaan²,

Al-Dhelaan³

2019

Computers, Materials &Amp; Continua

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“…When k-anonymity method is used to protect the privacy of users in the above query, if the anonymous region generated by the anonymous server is too large, the querying cost of the location-based service provider (LSP) will increase and the service quality deteriorates [17][18][19]. To solve this problem, the existing methods [20][21][22][23][24][25][26] obtain n disjoint anonymous subregions by removing the part that does not contain the users in the regions, to reduce the area of anonymous regions and improve the service quality as shown in Figure 1. However, the quality of service in LBS queries based on k-anonymity is not only related to the size of the anonymous regions but also to the user's query range.…”

Section: Introductionmentioning

confidence: 99%

Location Privacy Protection Research Based on Querying Anonymous Region Construction for Smart Campus

Sun

Yin

et al. 2018

Mobile Information Systems

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Along with the rapid development of smart campus, the deployment of novel learning applications for smart campus requires full consideration of information security issues. Location privacy protection is one of the most important issues, which considers the privacy protection and guarantees the quality of service. The existing schemes did not consider the area of the querying regions for location-based service provider (LSP) during the construction of the anonymous regions, which led to the low quality of service. To deal with this problem, the user’s query range was introduced to present a novel anonymous region construction scheme. In the proposal, the anonymous server first generated the original anonymous subregions according to the user’s privacy requirements, and then merged these subregions to construct the anonymity regions submitted to LSP based on the size of corresponding querying regions. The security and experiment analysis show that the proposed scheme not only protects the user’s privacy effectively but also decreases the area of LSP querying regions and the region-constructing time, improving the quality of service for smart campus.

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“…Deng W et al [28] investigated an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus. Rong H. et al [29] proposed a novel K + -isomorphism method and an improved MPD-V method. In addition, in order to accomplish the complex tasks, some current techniques are combined with the K +isomorphism method.…”

Section: Introductionmentioning

confidence: 99%

Deployment accuracy analysis of cable-strut deployable mechanism with joint clearances and forces constrained

Lin¹,

Chen²,

Chen³

et al. 2018

J. vibroeng.

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The effects on deployment accuracy of joint clearances and forces acting on the deployable mechanism are investigated. "A virtual massless link" is used to describe the joint clearance of the mechanism in the free state. Aiming at the following problems of multi-loop linkages mechanical system such as loop constraints coupling and complicated topology structure, the geometric relationships and the deploying error equations of deployable mechanism are provided based on Denavit-Hartenberg matrix by splitting multi-loop linkage into 3 single-loop linkages. In addition, with consideration of forces, "a virtual massless rope" is used instead of "virtual massless link" to describe the joint clearance for avoiding the extra singularities. And the criterion of mechanism in stable configuration is obtained by weighting coefficient method on the basis of minimum potential principle. The solving procedure of deploying error equations in the state of forces constrained is presented by combining Monte-Carlo method with genetic algorithm, it is an effective solution for the nonlinear objective function with multiple constraints and strong coupling. The results indicate that the appropriate forces acting on mechanism produce a significant effect on improving repeat accuracy, and the absolute accuracy can be improved with the future error compensation.

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A novel subgraph $$K^{+}$$ K + -isomorphism method in social network based on graph similarity detection

Cited by 58 publications

References 37 publications

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Location Privacy Protection Research Based on Querying Anonymous Region Construction for Smart Campus

Deployment accuracy analysis of cable-strut deployable mechanism with joint clearances and forces constrained

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