A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions. >

Temporal and real-time databases: a survey

Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare. Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. However, deciding on the allowable uses of data while preserving security and patient’s right to privacy is a difficult task. Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed. To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed the state-of-the-art security and privacy challenges in big data as applied to healthcare industry, assessed how security and privacy issues occur in case of big healthcare data and discussed ways in which they may be addressed. We mainly focused on the recently proposed methods based on anonymization and encryption, compared their strengths and limitations, and envisioned future research directions.

/pdf/big-healthcare-data-preserving-security-and-privacy-4tobs19dqr.pdf

Big healthcare data: preserving security and privacy

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

https://link.springer.com/content/pdf/10.1186%2Fs40537-016-0059-y.pdf

Big data privacy: a technological perspective and review

Task-role-based access control model

The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field. Here, we suggest two kinds of kernel methods for genic interaction extraction, considering the structural aspects of sentences. First, we improve our prior dependency kernel by modifying the kernel function so that it can involve various substructures in terms of (1) e-walks, (2) partial match, (3) non-contiguous paths, and (4) different significance of substructures. Second, we propose the walk-weighted subsequence kernel to parameterize non-contiguous syntactic structures as well as semantic roles and lexical features, which makes learning structural aspects from a small amount of training data effective. Furthermore, we distinguish the significances of parameters such as syntactic locality, semantic roles, and lexical features by varying their weights. We addressed the genic interaction problem with various dependency kernels and suggested various structural kernel scenarios based on the directed shortest dependency path connecting two entities. Consequently, we obtained promising results over genic interaction data sets with the walk-weighted subsequence kernel. The results are compared using automatically parsed third party protein-protein interaction (PPI) data as well as perfectly syntactic labeled PPI data.

/pdf/walk-weighted-subsequence-kernels-for-protein-protein-woffnk8edg.pdf

Walk-weighted subsequence kernels for protein-protein interaction extraction

As a large quantity of information is presented in XML format on the Web, there are increasing demands for XML security. Until now, research on XML security has been focused on the security of data communication using digital signatures or encryption technologies. As XML is also used for a data representation of data storage, XML security comes to involve not only communication security but also managerial security. Managerial security is guaranteed through access control, but existing XML access control models consider only read queries. These models may make some problems when unauthorized users try to change XML documents or their structure. Therefore the access control of update queries must be executed correctly and efficiently as well as read queries. In this paper, we discuss an XML access control model and propose a technique that supports not only read operations but also update operations. We define new action types to systematically manage complex information of access right and to process various update queries in an efficient manner. Using these action types, the system can save memory and other system resources that are used in DOM-based DTD verification process, and shortens the overall steps of access control by filtering unnecessary queries out at the early stage. Although for read queries the proposed access control model introduces a minor overhead in determining action types, for update queries it shows better performance compared to existing access control models.

/pdf/access-control-of-xml-documents-considering-update-3d0vjiv5rz.pdf

Access control of XML documents considering update operations

Scalable and efficient web services composition based on a relational database

Companies today manage business information with computer systems and many users access it for their job functions. Companies need security mechanisms to effectively protect important information. Moreover they need to minimize the interruptions from security mechanisms that cause delays in the execution of business activities. It is a difficult problem. In this paper we analyze the requirements of access control in enterprise environment and propose classifications for job functions. We propose a improved access control model for enterprise environment through integration of role based access control and activity based access control model.

Seog Park

Papers

Task-role-based access control model

Walk-weighted subsequence kernels for protein-protein interaction extraction

Access control of XML documents considering update operations

Scalable and efficient web services composition based on a relational database

Task-Role Based Access Control (T-RBAC): An Improved Access Control Model for Enterprise Environment