End-to-End Privacy for Open Big Data Markets

Perera, Charith; Ranjan, Rajiv; Wang, Lizhe

doi:10.1109/mcc.2015.78

Cited by 34 publications

(30 citation statements)

References 14 publications

(27 reference statements)

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“…Calculate the distance between V to V , (9) end if (10) end if (11) end for (12) end for (13) SorttheVMsin V in the increasing order of distance (14) end for (15) Return CV Algorithm 2: VM selection for data placement.…”

Section: Inputmentioning

confidence: 99%

“…As there are multiple data placement policies for each dataset, the placement policy with the minimum cost, calculated by formula (10), is selected as the final data placement strategy.…”

Section: Definition 3 (Data Placement Strategy Ofmentioning

confidence: 99%

“…= MAX , = 1 (8) while ≤ | V | do (9) if V , can hold then (10) Calculate the total cost TC by (10) (11) if TC < then (12) = TC (13) else = + 1 (14) end if (15) end if (16) end while (17) U p d a t e with cost C (18) end if (19) PM 2 separately, and the data transferring between these two VMs only employs the switch 4 .…”

Section: Inputmentioning

confidence: 99%

“…Currently, data privacy issues have received a lot of attention due to the increasing concern of the privacy and the data value protections, since the individuals often suffer heavy blows from privacy leaks [8,9]. How to protect data and improve the security level of private data has become a hot topic of cloud computing [10]. Generally, it is an effective way to place these datasets in the private cloud; thus hybrid cloud for big data storage should be taken into consideration for privacy-aware applications [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Zhao

Ruan

et al. 2017

Security and Communication Networks

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Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.

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Section: Inputmentioning

confidence: 99%

“…As there are multiple data placement policies for each dataset, the placement policy with the minimum cost, calculated by formula (10), is selected as the final data placement strategy.…”

Section: Definition 3 (Data Placement Strategy Ofmentioning

confidence: 99%

Section: Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Zhao

Ruan

et al. 2017

Security and Communication Networks

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“…Various existing studies have pointed at the risks and challenges of privacy violations for publishing and using open data (Kalidien, Choenni et al 2010, Conradie and Choenni 2014, Janssen and van den Hoven 2015, Perera, Ranjan et al 2015. Some studies have identified privacy risks or policies for organizations in collecting and processing data (Drogkaris, Gritzalis et al 2015, Kao 2015, some have provided decision support for opening data in general (Zuiderwijk and Janssen 2015), and some have focused on releasing information and data on the individual level (James, Warkentin et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Opening More Data - A New Privacy Risk Scoring Model for Open Data

Ali-Eldin

Eijk

Janssen

2017

Proceedings of the Seventh International Symposium on Business Modeling and Software Design

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Abstract:While the opening of data has become a common practice for both governments and companies, many datasets are still not published since they might violate privacy regulations. The risk on privacy violations is a factor that often blocks the publication of data and results in a reserved attitude of governments and companies. Additionally, even published data, which might seem privacy compliant, can violate user privacy due to the leakage of real user identities. This paper proposes a privacy risk scoring model for open data architectures to analyse and reduce the risks associated with the opening of data. The key elements consist of a new set of open data attributes reflecting privacy risks versus benefits trades-offs. Further, these attributes are evaluated using a decision engine and a scoring matrix intro a privacy risk indicator (PRI) and a privacy risk mitigation measure (PRMM). Privacy Risk Indicator (PRI) represents the predicted value of privacy risks associated with opening such data and privacy risk mitigation measures represent the measurements need to be applied on the data to avoid the expected privacy risks. The model is exemplified through five real use cases concerning open datasets.

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