Protecting Sensitive Labels in Social Network Data Anonymization

Yuan, Mingxuan; Chen, Lei; Yu, Philip S.; Yu, Ting

doi:10.1109/tkde.2011.259

Cited by 114 publications

(89 citation statements)

References 25 publications

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“…However, there are various privacy preserving techniques (i.e. anonymization techniques and data perturbation techniques) have been well studied in literature to preserve the privacy of individuals in the data sets ( [29]- [31] and references there in).…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Rahulamathavan

Veluru

Phan

et al. 2014

IEEE J. Biomed. Health Inform.

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract-A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends towards remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in encrypted form during the diagnosis process. Hence the server involved in the diagnosis process is not able to learn any extra knowledge about the patient data and results. Our experimental results on popular medical data sets from UCI database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised. Permanent repository link

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Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Rahulamathavan

Veluru

Phan

et al. 2014

IEEE J. Biomed. Health Inform.

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“…Drawback of proposed algorithm is that it still needs some improvements in order to reduce the complexity so that it can be applied to large social networks. Yuan et al [33] defined a k-degree ldiversity anonymity model for the protection of structural information and sensitive labels of people. Many privacy models like k-anonymity to prevent node reidentification through structure information have been proposed but an attacker may still be able to obtain private information of a person i.e.…”

Section: Fig 2 Social Network Graph With 7 Nodes and Sensitive Attrimentioning

confidence: 99%

Privacy Preserving Techniques in Social Networks Data Publishing - A Review

Singh¹,

Bansal²,

Sofat³

2014

IJCA

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“…Although researchers have proposed various anonymous models based on k-anonymity [6] to achieve privacy protection in existing research [7][8][9], the balance between privacy safety and data utility is still new in the field of social network publishing [4]. The existing approaches may prevent leakage of some privacy information when publishing social network data, but may result in nontrivial utility loss without exploring the attribute of sparse distribution and without recognizing the fact that different nodes have different impacts on the network structure.…”

Section: Introductionmentioning

confidence: 99%

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Xie

Zheng

2016

Algorithms

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Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users' privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.

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Protecting Sensitive Labels in Social Network Data Anonymization

Cited by 114 publications

References 25 publications

Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Privacy Preserving Techniques in Social Networks Data Publishing - A Review

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

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