Updated Template

bib_on_BigDataAccessControl.bib

@@ -86,7 +86,9 @@ @inproceedings{aissa2020decide
 	month        = {November},
 	booktitle    = {Proc. of EMCIS},
 	address      = {Dubai, EAU},
-	volume       = 402
+	volume       = 402,
+	pages        = {63--78},
+	organization = {Springer}
 }
 @inproceedings{5v,
 	title        = {Addressing big data issues in Scientific Data Infrastructure},
@@ -462,23 +464,30 @@ @inproceedings{IBE:2006
 	year         = 2006,
 	month        = {August},
 	booktitle    = {Proc. of CRYPTO},
-	address      = {Santa Barbara, USA}
+	address      = {Santa Barbara, USA},
+	pages        = {290--307}
 }
 @inproceedings{ABE1:2004,
 	title        = {Fuzzy Identity-Based Encryption},
 	author       = {Amit Sahai and Brent Waters},
 	year         = 2005,
 	month        = {May},
 	booktitle    = {Proc. of EUROCRYPT},
-	address      = {Aarhus, Denmark}
+	address      = {Aarhus, Denmark},
+	pages        = {457--473},
+	abstract     = {We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, $\omega$, to decrypt a ciphertext encrypted with an identity, $\omega$ {\textasciiacutex}, if and only if the identities $\omega$ and $\omega$ {\textasciiacutex} are close to each other as measured by the ``set overlap'' distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term ``attribute-based encryption''.}
 }
 @inproceedings{ABE2:2006,
 	title        = {Attribute-Based Encryption for Fine-Grained Access Control of Encrypted Data},
 	author       = {Goyal, Vipul and Pandey, Omkant and Sahai, Amit and Waters, Brent},
 	year         = 2006,
 	month        = 11,
 	booktitle    = {Proc. of ACM CCS},
-	address      = {Alexandria, USA}
+	publisher    = {ACM},
+	address      = {Alexandria, USA},
+	pages        = {89--98},
+	numpages     = 10,
+	keywords     = {hierarchical identity-based encryption, broadcast encryption, attribute-based encryption, delegation, audit logs, access control}
 }
 @article{HOMOMEnc:2015,
 	title        = {A Guide to Fully Homomorphic Encryption},
@@ -494,9 +503,13 @@ @article{k-anon
 	year         = 2001,
 	journal      = {IEEE transactions on Knowledge and Data Engineering},
 	publisher    = {IEEE},
+	address      = {USA},
 	volume       = 13,
 	number       = 6,
-	pages        = {1010--1027}
+	pages        = {1010--1027},
+	issue_date   = {October 2002},
+	abstract     = {Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection.},
+	keywords     = {re-identification, data anonymity, data fusion, data privacy, privacy}
 }
 @article{s.tkde01,
 	title        = {Protecting Respondents' Identities in Microdata Release},
@@ -514,7 +527,9 @@ @incollection{DEMCHENKO201721
 	booktitle    = {Big Data Analytics for Sensor-Network Collected Intelligence},
 	publisher    = {Academic Press},
 	series       = {Intelligent Data-Centric Systems},
-	pages        = {21--62}
+	pages        = {21--62},
+	keywords     = {Big Data, Big Data Architecture Framework (BDAF), Big data infrastructure, NIST Big Data Architecture (BDRA), Cloud computing, Intercloud Architecture Framework (ICAF), Cloud powered design, SlipStream cloud automation platform},
+	abstract     = {This chapter describes the general architecture and functional components of the cloud-based big data infrastructure (BDI). The chapter starts with the analysis of emerging Big Data and data intensive technologies and provides the general definition of the Big Data Architecture Framework (BDAF) that includes the following components: Big Data definition, Data Management including data lifecycle and data structures, generically cloud based BDI, Data Analytics technologies and platforms, and Big Data security, compliance, and privacy. The chapter refers to NIST Big Data Reference Architecture (BDRA) and summarizes general requirements to Big Data systems described in NIST documents. The proposed BDI and its cloud-based components are defined in accordance with the NIST BDRA and BDAF. This chapter provides detailed analysis of the two bioinformatics use cases as typical example of the Big Data applications that have being developed by the authors in the framework of the CYCLONE project. The effective use of cloud for bioinformatics applications requires maximum automation of the applications deployment and management that may include resources from multiple clouds and providers. The proposed CYCLONE platform for multicloud multiprovider applications deployment and management is based on the SlipStream cloud automation platform and includes all necessary components to build and operate complex scientific applications. The chapter discusses existing platforms for cloud powered applications development and deployment automation, in particularly referring to the SlipStream cloud automation platform, which allows multicloud applications deployment and management. The chapter also includes a short overview of the existing Big Data platforms and services provided by the major cloud services providers which can be used for fast deployment of customer Big Data applications using the benefits of cloud technologies and global cloud infrastructure.}
 }
 @article{laney20013d,
 	title        = {3D data management: Controlling data volume, velocity and variety},
@@ -532,12 +547,17 @@ @book{SHINDER2013187
 	year         = 2013,
 	booktitle    = {Windows Server 2012 Security from End to Edge and Beyond},
 	publisher    = {Syngress},
-	pages        = {187--210}
+	address      = {Boston},
+	pages        = {187--210},
+	keywords     = {Dynamic Access Control, Picture Password},
+	abstract     = {Access control requires both authentication and authorization. Windows Server 2012 includes a number of new features and technologies that enhance your ability to control access to information contained on your network file servers. The major advance in this area is Dynamic Access Control, which makes it possible for you to use claims-based authorization for accessing information on your file servers. This means that you are no longer limited to conventional user/group-based access control. Windows 8 and Windows Server 2012 also introduced a new method for logging onto computers called Picture Password. With Picture Password, the user can select a picture and then perform a number of gestures on the picture. The picture and the gestures are packaged with the user's user name and password. The user can then log on by being presented with the picture and repeating the gestures, which will open the package and expose the user name and password to the system. In this chapter, we will look at how these technologies work and how to implement them.}
 }
 @article{Byun2008,
 	title        = {Purpose based access control for privacy protection in relational database systems},
 	author       = {Byun, Ji-Won and Li, Ninghui},
 	year         = 2008,
+	month        = {Jul},
+	day          = {01},
 	journal      = {The VLDB Journal},
 	volume       = 17,
 	number       = 4,
@@ -664,19 +684,18 @@ @article{Colombo:JournCybersec:2019
 %%%%%%%FROM MEDES%%%%%%%
 %%%%% FROM MEDES 2 %%%%%%%%%
 %%%%%%% FROM MEDES2 1 %%%%%%%
-
 @article{RAHMAN20102707,
-title = {Jaccard Index based availability prediction in enterprise grids},
-journal = {Procedia Computer Science},
-volume = {1},
-number = {1},
-pages = {2707-2716},
-year = {2010},
-note = {ICCS 2010},
-issn = {1877-0509},
-doi = {https://doi.org/10.1016/j.procs.2010.04.304},
-url = {https://www.sciencedirect.com/science/article/pii/S1877050910003054},
-author = {Mustafizur Rahman and Md. Rafiul Hassan and Rajkumar Buyya},
-keywords = {Availability, Data mining, Forecasting, Enterprise grid, Jaccard Index},
-abstract = {Enterprise Grid enables sharing and aggregation of a set of computing or storage resources connected by enterprise network, but the availability of the resources in this environment varies widely. Thus accurate prediction of the availability of these resources can significantly improve the performance of executing compute-intensive complex scientific and business applications in enterprise Grid environment by avoiding possible runtime failures. In this paper, we propose a Jaccard Index based prediction approach utilizing lazy learning algorithm that searches for a best match of a sequence pattern in the historical data in order to predict the availability of a particular machine in the system. We compare it against three other well known availability prediction techniques using simulation based study. The experimental results show that our Jaccard Index based prediction approach achieves better prediction accuracy with reduced computational complexity when compared to other similar techniques.}
+	title        = {Jaccard Index based availability prediction in enterprise grids},
+	author       = {Mustafizur Rahman and Md. Rafiul Hassan and Rajkumar Buyya},
+	year         = 2010,
+	journal      = {Procedia Computer Science},
+	volume       = 1,
+	number       = 1,
+	pages        = {2707--2716},
+	doi          = {https://doi.org/10.1016/j.procs.2010.04.304},
+	issn         = {1877-0509},
+	url          = {https://www.sciencedirect.com/science/article/pii/S1877050910003054},
+	note         = {ICCS 2010},
+	keywords     = {Availability, Data mining, Forecasting, Enterprise grid, Jaccard Index},
+	abstract     = {Enterprise Grid enables sharing and aggregation of a set of computing or storage resources connected by enterprise network, but the availability of the resources in this environment varies widely. Thus accurate prediction of the availability of these resources can significantly improve the performance of executing compute-intensive complex scientific and business applications in enterprise Grid environment by avoiding possible runtime failures. In this paper, we propose a Jaccard Index based prediction approach utilizing lazy learning algorithm that searches for a best match of a sequence pattern in the historical data in order to predict the availability of a particular machine in the system. We compare it against three other well known availability prediction techniques using simulation based study. The experimental results show that our Jaccard Index based prediction approach achieves better prediction accuracy with reduced computational complexity when compared to other similar techniques.}
 }