These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge for the user without forfeiting the security and privacy of individuals crude information. We devised two unique protocols for frequent mining itemsets in horizontally partitioned datasets while maintaining privacy. In such a scenario, data possessors outwork mining tasks on their multiparty data by preserving privacy. The proposed framework model encompasses two or more data possessors who encrypt their information and dispense their encrypted data to two or more clouds by a data share allocator algorithm. This methodology protects the data possessor raw data from other data possessors and the other clouds. To guarantee data privacy, we plan a proficient enhanced homomorphic encryption conspire. Our approach ensures privacy during communication and accumulation of data and guarantees no information or data adversity and no incidental consequences for data utility.

/pdf/preserving-data-confidentiality-in-association-rule-mining-3uv0aq99.pdf

Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

In this paper, we develop a user-centric privacy framework for quantitatively assessing the exposure of personal information in open settings. Our formalization addresses key-challenges posed by such open settings, such as the unstructured dissemination of heterogeneous information and the necessity of user- and context-dependent privacy requirements. We propose a new definition of information sensitivity derived from our formalization of privacy requirements, and, as a sanity check, show that hard non-disclosure guarantees are impossible to achieve in open settings. After that, we provide an instantiation of our framework to address the identity disclosure problem, leading to the novel notion of d-convergence. d-convergence is based on indistinguishability of entities and it bounds the likelihood with which an adversary successfully links two profiles of the same user across online communities. Finally, we provide a large-scale evaluation of our framework on a collection of 15 million comments collected from the Online Social Network Reddit. Our evaluation validates the notion of d-convergence for assessing the linkability of entities in our data set and provides deeper insights into the data set's structure.

How well do you blend into the crowd? - d-convergence: A novel paradigm for quantifying privacy in the age of Big-Data.

Data mining is an important part of information management technology. Simply put, it is a method to extract and analyze meaningful patterns and correlations in a large relational database. In Data mining, Decision trees are one of the most worldwide used tools for decision support. In the emerging area of Data mining applications, users of data mining tools are faced with the problem of data sets that are comprised of large numbers of features and instances. Such kinds of data sets are not easy to handle for mining because decision trees generally depends on several parameters like dataset used and configuration of the tree itself among others in order to build an accurate model classification. In this work a novel hybrid classifier system is presented for improving accuracy of decision trees using clustering techniques. This system is formed by a clustering algorithm, a decision tree and an optional module for identifying appropriate parameters for the clustering algorithm. These three modules working together are capable to increase the accuracy of the solutions. The validation of the results of this work has been performed using several well-known datasets and applying two decision trees algorithms. The accuracy percentages are compared in order to show our proposal improvement, obtaining good results. Finally two clustering algorithms have been used to compare the accuracy between different proposals.

/pdf/improving-accuracy-of-decision-trees-using-clustering-96l0b6okjq.pdf

Improving Accuracy of Decision Trees Using Clustering Techniques

Dayana P. B. SPAGNUELO Defining, Measuring, and Enabling Transparency for Electronic Medical Systems Transparency is a novel concept in the context of Information and Communication Technology (ICT). It has arisen from regulations as a data protection principle, and it is now being studied to encompass the peculiarities of digital information. Transparency, however, is not the first security concept to be borrowed from regulations; privacy once emerged from discussions on individual’s rights. Privacy began to be vigorously debated in 1890, when Warren and Brandeis analysed legal cases for which penalties were applied on the basis of defamation, infringement of copyrights, and violation of confidence [244]. The authors defended that those cases were, in fact, built upon a broader principle called privacy. But privacy was only given a structured definition almost one century later, in 1960, when Prosser examined cases produced after Warren and Brandeis’ work [176], classifying violation of privacy into four different torts; it took twenty years more before the concept was thoroughly studied for its functions in ICT. Guidelines by the OECD outlined principles to support the discussion of privacy as a technical requirement [56]. Proceeded by international standards for a privacy framework (ISO/IEC 29100), which translated the former legal concepts into information security terms, such as data minimisation, accuracy, and accountability. Transparency has a younger, but comparable history; the current General Data Protection Regulation (GDPR) defines it as a principle which requires “that any information and communication relating to the processing of those personal data be easily accessible and easy to understand [..]". However, other related and more abstract concepts preceded it. In the Health Insurance Portability and Accountability Act (HIPAA) of 1996, the Privacy Rule demands to document privacy policies and procedures and to notify individuals of uses of their health information. Former European Directives, i.e., 95/46/EC and 2011/24/EU, establish “the right for individuals to have access to their personal data concerning their health [..] also in the context of cross-border healthcare”. The same did the Freedom of Information Act (FOIA) of 1966, instituting that any person has a right to obtain from agencies information regarding their records. These and other similar requests refer to the transversal quality called transparency. Similarly to what happened with privacy, transparency was also the subject of guidelines that clarify its interpretation in ICT [10]. However, no framework or standard has been defined yet that translates transparency into a technical property. This translation is the goal of our work. This thesis is dedicated to debate existing interpretations for transparency, to establish requirements and measurement procedures for it, and to study solutions that can help systems adhere to the transparency principle from a technical perspective. Our work constitutes an initial step towards the definition of a framework that helps accomplish meaningful transparency in the context of Electronic Medical Systems.

/pdf/defining-measuring-and-enabling-transparency-for-electronic-4mg27b79a1.pdf

Defining, Measuring, and Enabling Transparency for Electronic Medical Systems

In the developed countries the concern of using personal information or data of individuals for intrusive or malicious purposes has given rise to the necessity for preserving and protecting the privacy of individuals and consumers from such potential misuse In developing countries like India the issue of privacy as it is related to individual consumers has not been of much importance With the recent advances in the field of data mining the concerns regarding privacy have increased because it is now possible to use commonly available data mining software on publicly available data to unearth information about individuals that may be considered private and this is all the more true in developing countries like India In this paper, we look at the existing privacy policies of some top Indian companies in the telecom, banking and insurance sectors Privacy preserving data mining PPDM is discussed and a framework to apply PPDM methods in chosen domains is suggested

Towards a privacy preserving data mining framework - an Indian perspective

The goal of the paper is to present different approaches to privacy-preserving data sharing and publishing in the context of e-health care systems. In particular, the literature review on technical issues in privacy assurance and current real-life high complexity implementation of medical system that assumes proper data sharing mechanisms are presented in the paper.

Privacy-preserving Data Mining, Sharing and Publishing

The goal of the paper is to present different appro aches to privacy-preserving data sharing and publis hing in the context of e-health care systems. In particular, th e literature review on technical issues in privacy assurance and current real-life high complexity implementation of medical system that assumes proper data sharing mechanisms are presented in the paper.

Privacy-preserving data mining, sharing and publishing

A discussion on matters arising from encountered problems while designing and introducing e-health systems is presented in this paper. In particular, some difficulties in the adaption of ICT to e-health systems have been emphasised. Additionally, the future vision of healthcare evolution by means of information technology is analysed. Questions which arose as to the main challenges for healthcare systems were both technological and non-technological.

Current Trends and Difficulties in Knowledge-Based e-Health Systems

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Privacy-preserving Data Mining, Sharing and Publishing

Privacy-preserving data mining, sharing and publishing

Current Trends and Difficulties in Knowledge-Based e-Health Systems