12,574 research outputs found
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study
The authors would like to thank the anonymous reviewers and editors who
have been involved in examining this manuscript.Background: According to the renowned and Oscar award-winning American actor and film
director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute
prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences
caused by privacy and data breaches. The Indian healthcare industry is one of the largest and
rapidly developing industry. Overall, healthcare management is changing from disease-centric into
patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management,
and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents
the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy
problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare
systems. The case study of the NITI Aayog report is presented to explain how reformation occurs
in Indian healthcare systems. Findings: It is found that there have been numerous research studies
conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare,
specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large
population, and existing infrastructures. This paper reviews the Indian healthcare system and the
applications that drive it. Additionally, the paper also maps that how privacy issues are happening
in every healthcare sector in India. Originality/Value: To understand these factors and gain insights,
understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found
no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues.
The paper is original in terms of its overview of the healthcare system and privacy issues. Social
Implications: Privacy has been the most ignored part of the Indian healthcare system. With India
being a country with a population of 130 billion, much healthcare data are generated every day. The
chances of data breaches and other privacy violations on such sensitive data cannot be avoided as
they cause severe concerns for individuals. This paper segregates the healthcare system’s advances
and lists the privacy that needs to be addressed first
Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming
P2DM-RGCD: PPDM Centric Classification Rule Generation Scheme
In present day applications the approach of data mining and associated privacy preservation plays a significant role for ensuring optimal mining function. The approach of privacy preserving data mining (PPDM) emphasizes on ensuring security of private information of the participants. On the contrary majority of present mining applications employ the vertically partitioned data for mining utilities. In such scenario when the overall rule is divided among participants, some of the parties remain with fewer rules sets and thus the classification accuracy achieved by them always remain questionable. On the other hand, the consideration of private information associated with any part will violate the approach of PPDM. Therefore, in order to eliminate such situations and to provide a facility of rule regeneration in this paper, a highly robust and efficient rule regeneration scheme has been proposed ensures optimal classification accuracy without using any critical user information for rule generation. The proposed system developed a rule generation function called cumulative dot product (P2DM-RGCD) rule regeneration scheme. The developed algorithm generates two possible optimal rule generation and update functions based on cumulative updates and dot product. The proposed system has exhibited optimal response in terms of higher classification accuracy, minimum information loss and optimal training efficiency
On the Measurement of Privacy as an Attacker's Estimation Error
A wide variety of privacy metrics have been proposed in the literature to
evaluate the level of protection offered by privacy enhancing-technologies.
Most of these metrics are specific to concrete systems and adversarial models,
and are difficult to generalize or translate to other contexts. Furthermore, a
better understanding of the relationships between the different privacy metrics
is needed to enable more grounded and systematic approach to measuring privacy,
as well as to assist systems designers in selecting the most appropriate metric
for a given application.
In this work we propose a theoretical framework for privacy-preserving
systems, endowed with a general definition of privacy in terms of the
estimation error incurred by an attacker who aims to disclose the private
information that the system is designed to conceal. We show that our framework
permits interpreting and comparing a number of well-known metrics under a
common perspective. The arguments behind these interpretations are based on
fundamental results related to the theories of information, probability and
Bayes decision.Comment: This paper has 18 pages and 17 figure
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