Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Zheng, Xu; Cai, Zhipeng; Yu, Jiguo; Wang, Chaokun; Li, Yingshu

doi:10.1109/jiot.2017.2679483

Cited by 131 publications

(60 citation statements)

References 25 publications

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“…For example, whether two individuals in a social network have a close relationship may be expected to be kept a secret. Therefore, privacy concerns have been raised in increasingly emerging technologies [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

Self Cite

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“…(3) is the LSSS matrix, initialized to null; () is the function that maps the rows of to attributes. (4) BooleanFormula2LSSS(root( ),V, , , ()); (5) function (BooleanFormula2LSSS ( , V, , , ())) (6) if is an AND gate then (7) pad V with 0's (if necessary) to make its length = ; (8) label the left child with the vector V ‖ 1, where ‖ means concatenation; (9) label the right child with the vector (0, . .…”

Section: Resultsmentioning

confidence: 99%

“…However, despite the attractive features provided by smart grid technologies, challenges, especially those in cyber security and privacy [5], are still present. For example, it has been reported that the pervasively adopted integrated Supervisory Control and Data 2 Wireless Communications and Mobile Computing Acquisition (SCADA)/Energy Management systems [6] are vulnerable to significant security threats [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Secure and Scalable Data Communication Scheme in Smart Grids

Liu

et al. 2018

Wireless Communications and Mobile Computing

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The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role-based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

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“…e following sections describe how IoT handles personal information specifically, but this chapter explains the rough privacy issues occurred in IoT environment. Due to the close correlation between personal physical characteristics and status, the adoption of cyberphysical-social systems (CPSSs) has inevitably been challenged by users' privacy concerns [13]. e first step is about collecting data.…”

Section: Iot Privacy Issuesmentioning

confidence: 99%

An Analysis of Economic Impact on IoT Industry under GDPR

Seo

Kim

Park

et al. 2018

Mobile Information Systems

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e EU GDPR comes into effect on May 25, 2018. Under this regulation, stronger legislation than the existing directive can be enforced. e IoT industry, especially among various industries, is expected to be heavily influenced by GDPR since it uses diverse and vast amounts of personal information. is paper first analyzes how the IoT industry handles personal information and summarizes why it is affected by GDPR. e paper then uses the cost definition of Gordon and Loeb model to estimate how GDPR affects the cost of IoT firms qualitatively and uses the statistical and legal bases to estimate quantitatively. From a qualitative point of view, GDPR impacted the preventative cost and legal cost of the Gordon and Loeb model. Quantitative view showed that the cost of IoT firms after GDPR could increase by three to four times on average and by 18 times if the most. e study finally can be applied to situational awareness of the economic impact on the certain industry.

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Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Cited by 131 publications

References 25 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

A Secure and Scalable Data Communication Scheme in Smart Grids

An Analysis of Economic Impact on IoT Industry under GDPR

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