Differential Privacy Preserving in Big Data Analytics for Connected Health

Lin, Chi; Song, Zihao; Song, Houbing; Zhou, Yanhong; Wang, Yi; Wu, Guowei

doi:10.1007/s10916-016-0446-0

Cited by 79 publications

(43 citation statements)

References 12 publications

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“…To et al [13] introduced a novel privacy-aware framework for spatial crowdsourcing, which enables the participation of workers without compromising their location privacy. Focusing on the privacy protection of sensitive information in body area networks, the authors in [14,15] proposed different privacy preserving schemes, based on differential privacy model, via a tree structure and dynamic noise thresholds, respectively. The work in [16] proposed a novel differentially private frequent sequence mining algorithm by leveraging a sampling-based candidate pruning technique, which satisfies -differential privacy and can privately find frequent sequences with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Differential privacy (DP), a privacy preserving model originated from statistical database, has currently drawn considerable attentions in research communities [10][11][12][13][14][15][16][17] due to (i) its rigorous and provable privacy guarantee and (ii) its assumption of adversaries' arbitrary background knowledge. However, DP actually assumes that the tuples in databases are independent [18].…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

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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“…A differential privacy protection scheme for big data in body sensor network is introduced by [8], based on the concept of dynamic noise thresholds. The interference threshold is calculated for each data arrival in order to add noise to data.…”

Section: It Finds and Deletes Personal Sensitive Information Form Thementioning

confidence: 99%

A comparative study of recent advances in Big data Security and Privacy

Kourid¹,

Chikhi²,

Hong³

2017

AJMAHS

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Big data is coming with new challenges in security; involve the three aspects of security: (confidentiality, availability, integrity) and privacy. These chal-lenges are due to the characteristics 5V of data in Big data: velocity, variety, volume, value, and veracity. And depend on several level of security: network, data, applica-tion, and authentication. Furthermore, big data is also promising security. The huge amount of data provides a more security information like data logs. Moreover, big da-ta analysis can be applied to security. Many theories for big data security are pro-posed in literature, covering the different aspects of security and privacy. Recently, different schemes and frameworks are introduced to reach high level of security in big data, based on different security theories. In this paper, we discuss different challeng-es in big data security and privacy, and we introduce recent security theories and works in this filed. A comparative study of latest advances in big data security and privacy is presented.

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“…Nowadays, the representative method suitable for Big Data access control is identity based access control and attribute based access control [5].…”

Section: Access Control Technologymentioning

confidence: 99%

Overview of Personal Privacy Protection Technology in Big Data

Mao-Qiang¹,

Fan²

2016

dtetr

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Abstract. Big Data called new oil in the future is affecting people's life. It has attracted much attention from academy, industry and national government departments. But there are many security problems in the collection, storage, mining and displaying of Big Data and privacy protection in it is directly related to the interests of user. Firstly, this paper will introduce concept of Big Data and analyzes many information security problems in it. Then, it will discuss the key privacy protection technologies and recent advances in anonymous protection, data encryption, access control, cloud storage location privacy and legal regulations. Finally, this paper will summarize and discuss further study of Big Data privacy protection.

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Differential Privacy Preserving in Big Data Analytics for Connected Health

Cited by 79 publications

References 12 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

A comparative study of recent advances in Big data Security and Privacy

Overview of Personal Privacy Protection Technology in Big Data

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