Big Data Privacy Research Articles

AKILLI KENTLERDE VERİNİN GİZLİLİĞİ VE GÜVENLİĞİ: İLKELER VE YAKLAŞIMLAR

Innovations emerging in the dynamic environment of technological developments lead to new situations in social and economic fields. Cities are one of the important areas where these developments are reflected. Thus, smart cities and big data concepts come up and these developments cause changes in services provided in cities. However, these developments bring along some negativities, as well. The main purpose of this study is to examine the approaches and principles that have come to fore in order to ensure the privacy and security of big data in smart cities. Basically, the answer to this question is sought: what principles, approaches and practices come to the forefront in order to ensure the confidentiality and security of big data that is an important part of smart cities? The answer is sought through the relevant literature, legal regulations and international decisions. According to the general results of the study, it is understood that there are not any consensual standards on data security at global level. However, some regulations draw attention. At this point, the “General Data Protection Regulation (GDPR)” which came into force in 2018 is important. However, some principles and approaches are gaining importance. At the end of the study, some suggestions about policies are made according to the results obtained

An Optimized Integrated Framework of Big Data Analytics Managing Security and Privacy in Healthcare Data

Big data analytics has anonymously changed the overall global scenario to discover knowledge trends for future decision making. In general, potential area of big data application tends to be healthcare, where the global burden is to improve patient diagnostic system and providing patterns to assure the privacy of the end users. However, data constraints exists on real data which needs to be accessed while preserving the security of patients for further diagnostic analysis. This advancement in big data needs to addressed where the patient right needs to maintained while the disclosure of knowledge discovery for future needs are also addressed. To, embark and acknowledge the big data environment its adherently important to determine the cutting-edge research which can benefit end users and healthcare practioners to discover overall prognosis and diagnosis of disease while maintaining the concerns for privacy and security of patient data. In current state of art, we tried to address the big data analytics approach while maintain privacy of healthcare databases for future knowledge discovery. The current objective was to design and develop a novel framework which can integrate the big data with privacy and security concerns and determine knowledgably patterns for future decision making. In the current study we have utilized big data analytical technique for patients suffering from Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) coinfection to develop trends and detect patterns with socio economic factors. Further, a novel framework was implemented using unsupervised learning technique in STATA and MATLAB 7.1 to develop patterns for knowledge discovery process while maintain the privacy and security of data. The study overall can benefit end users to predict future prognosis of disease and combinatorial effects to determining varied policies which can assist patients with needs.

Evolutionary Computing Environments: Implementing Security Risks Management and Benchmarking

This research makes a focus on evolutionary computing environments i.e. Pervasive, Internet of Things, Artificially Intelligent (AI) and Big Data risks management. The contribution of paper lies in identifying, managing risks in advanced computing paradigms, mitigating security risks and benchmarking smart softwares. As the security and privacy of big volume data is heated discussion these days due to several vulnerabilities in data posed by its pervasive nature so the paper has implemented the security risks mitigation in big data projects. In addition, the benchmarking of pervasive software is done with the help of an intelligent tool for detecting application frameworks and security vulnerabilities. The application is scanned, and the intelligent tool quantifies the severity levels to provide the possible solutions. This tool also collects application metrices to benchmark it against technology, application business drivers and properties to improve the quantitative Performance of the software. This research also puts an analytical foundation for various risks management concepts in evolutionary environments i.e. Pervasive, AI, Internet of Things and Big Data.

Big Data and Big Risk: A Four-Factor Framework for Big Data Security and Privacy

Big Data and Big Risk: A Four-Factor Framework for Big Data Security and Privacy

Mathematics as a Tool of Manipulation in Modern Society. Review of the book by Cathy O’Neil «Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy»

The article focuses on the analysis of big data phenomenon that by the expansion of information technology has become a challenge for sociology and social statistics. The history of «big data» term origins is presented, the factors of appearance and development of this phenomenon are determined. It is noted that in the sociological perspective big data have not only transformed the methods of obtaining primary sociological information, but also changed the very logic of the study. The author considers that with a help of big data sociology will be able to return to its calling – the creation of a large theory of society, which, in turn, the opportunity to analyze and interpret big data depends on. Emphasis is placed on Cathy O'Neil’s work «Big data. Weapons of Math Destruction…», which concludes that big data construct new forms of inequality in a contemporary world. It is stressed that through the focus of this idea the American researcher analyzes the impact of big data on various spheres of public life: on the educational system, emphasizing the role of university rankings (which definition is not always transparent) in commercialization of higher education, its turning into big business; on the law enforcement system, in particular in a country such as the United States, noting that the mathematical models developed for the country's police have discrimination grounds for poor and «colored» citizens; on the system of employment, credit system, etc. At the same time, she writes that because of big data privacy is disappearing in people's lives, they are increasingly adapting to models of mass behavior, being under the influence of consumer and political (what is especially alarming) marketing. The author of the publication notes that Cathy O'Neil, unfortunately, gives no answer to the question of how it is possible to counteract the manipulative effects of big data. She relies heavily on ethical regulators and recommends to data specialists create models with mandatory forward linkages.

Preservation of Privacy of Big Data Using Efficient Anonymization Technique

Big data needs to be kept private because of the increase in the amount of data. Data is generated from social networks, organizations and various other ways, which is known as big data. Big data requires large storage as well as high computational power. At every stage, the data needs to be protected. Privacy preservation plays an important role in keeping sensitive information protected and private from any attack. Data anonymization is one of the techniques to anonymize data to keep it private and protected, which includes suppression, generalization, and bucketization. It keeps personal and private data anonymous from being known by others. But when it is implemented on big data, these techniques cause a great loss of information and also fail in defense of the privacy of big data. Moreover, for the scenario of big data, the anonymization should not only focus on hiding but also on other aspects. This paper aims to provide a technique that uses slicing, suppression, and functional encryption together to achieve better privacy of big data with data anonymization.

Büyük Veri, Yapay Zeka ve Makina Öğrenimi Algoritmalar: Hakikat Ötesi Çağda Dijital Tehditlerin Betimleyici bir Analizi

Did the utilization of big data change everything about how information circulates? Our digital data have been kept in big warehouses that we name ‘big data’. All the things that we do in virtual life leave a digital footprint and thanks to machine learning algorithms; in our newsfeed, we mostly face contents, which are similar to the subjects that we looked for before. Big data are being used for manipulating people to buy new products, to travel to new places, to read new books, etc. However, as it emerged in 2016 with Cambridge Analytica Scandal of Facebook, sometimes those technologies construct a threat for democracy. The underlying reason is that in our days, big data and AI algorithms have been used by political campaign managers to manipulate and/or persuade people through diffusion of promoted ‘false’ contents. The aim of this study, by doing a descriptive analysis of very recent historical events like the failure of Microsoft’s AI Tay and Youtube’s effects on the presidential election in Brazil, is to define very current digital threats against democracy. Additionally, to better describe and discuss these digital threats we did semi-structured interviews with four experts who work on AI algorithms, big data, and social engineering. Our analyses and findings that we gathered from semi-structured interviews showed that there are several digital threats in the post-truth era that we live in like digital manipulation, violation of data privacy and misuse of big data, personalized search engine algorithms that create filter bubbles, the ease of production and diffusion of fake contents.

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.

Big Data, Algorithms and Health Data

The most recent decade featured a data revolution in the healthcare sector in screening, monitoring and coordination of aid. Big data analytics have revolutionarized the medical profession. The health sector relys on Artificial Intelligence (AI) and robotics as never before. The opportunities of unprecedented access to healthcare, rational precision and human resemblance but also targeted aid in decentralized aid grids are obvious innovations that will lead to most sophisticated neutral healthcare in the future. Yet big data driven medical care also bears risks of privacy infringements and ethical concerns of social stratification and discrimination. Today’s genetic human screening, constant big data information amalgamation as well as social credit scores pegged to access to healthcare also create the most pressing legal and ethical challenges of our time. The call for developing a legal, policy and ethical framework for using AI, big data, robotics and algorithms in healthcare has therefore reached unprecedented momentum. Problematic appear compatibility glitches in the AI-human interaction as well as a natural AI preponderance outperforming humans. Only if the benefits of AI are reaped in a master-slave-like legal frame, the risks associated with these novel superior technologies can be curbed. Liability control but also big data privacy protection appear important to secure the rights of vulnerable patient populations. Big data mapping and social credit scoring must be met with clear anti-discrimination and anti-social stratification ethics. Lastly, the value of genuine human care must be stressed and precious humanness in the artifical age conserved alongside coupling the benefits of AI, robotics and big data with global common goals of sustainability and inclusive growth. The report aims at helping a broad spectrum of stakeholders understand the impact of AI, big data, algorithms and health data based on information about key opportunities and risks but also future market challenges and policy developments for orchestrating the concerted pursuit of improving healthcare excellence. Stateshuman and diplomates are invited to consider three trends in the wake of the AI (r)evolution: Artificial Intelligence recently gained citizenship in robots becoming citizens: With attributing quasi-human rights to AI, ethical questions arise of a stratified citizenship. Robots and algorithms may only be citizens for their protection and upholding social norms towards human-like creatures that should be considered slave-like for economic and liability purposes without gaining civil privileges such as voting, property rights and holding public offices. Big data and computational power imply unprecedented opportunities for: crowd understanding, trends prediction and healthcare control. Risks include data breaches, privacy infringements, stigmatization and discrimination. Big data protection should be enacted through technological advancement, self-determined privacy attention fostered by e-education as well as discrimination alleviation by only releasing targeted information and regulated individual data mining capacities. The European Union should consider establishing a fifth trade freedom of data by law and economic incentives: in order to bundle AI and big data gains large scale. Europe holds the unique potential of offering data supremacy in state-controlled universal healthcare big data wealth that is less fractionate than the US health landscape and more Western-focused than Asian healthcare. Europe could therefore lead the world on big data derived healthcare insights but should also step up to imbuing humane societal imperatives on these most cutting-edge innovations of our time.

Editor’s Note: Special Issue on Big Data Analytics and Privacy

Editor’s Note: Special Issue on Big Data Analytics and Privacy

D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

Privacy engineering for learning analytics in a global market

Purpose Privacy is a culturally universal process; however, in the era of Big Data privacy is handled very differently in different parts of the world. This is a challenge when designing tools and approaches for the use of Educational Big Data (EBD) and learning analytics (LA) in a global market. The purpose of this paper is to explore the concept of information privacy in a cross-cultural setting to define a common point of reference for privacy engineering. Design/methodology/approach The paper follows a conceptual exploration approach. Conceptual work on privacy in EBD and LA in China and the west is contrasted with the general discussion of privacy in a large corpus of literature and recent research. As much of the discourse on privacy has an American or European bias, intimate knowledge of Chinese education is used to test the concept of privacy and to drive the exploration of how information privacy is perceived in different cultural and educational settings. Findings The findings indicate that there are problems using privacy concepts found in European and North-American theories to inform privacy engineering for a cross-cultural market in the era of Big Data. Theories based on individualism and ideas of control of private information do not capture current global digital practice. The paper discusses how a contextual and culture-aware understanding of privacy could be developed to inform privacy engineering without letting go of universally shared values. The paper concludes with questions that need further research to fully understand information privacy in education. Originality/value As far as the authors know, this paper is the first attempt to discuss – from a comparative and cross-cultural perspective – information privacy in an educational context in the era of Big Data. The paper presents initial explorations of a problem that needs urgent attention if good intentions of privacy supportive educational technologies are to be turned into more than political slogans.

A Study of the Cryptographic Technologies on Big Data Privacy and Security in E-Commerce

In this era, big data is the most common buzzword across different industries due to its capabilities of collecting, processing, storing and analysing data. The advancement of the E-Commerce paved the way for merchants and customers to meet online to satisfy their requirements by exchanging goods and services at a reasonable cost. The challenges and opportunities for big data on the emphasis of data privacy and security is a widely discussed topic among businesses especially E-Commerce merchants. There are several reviews available on emphasizing big data opportunities and challenges with regard to privacy and security. However, a comprehensive review on E-Commerce highlighting thematically on the tools and technologies is not given enough consideration. Therefore, the purpose of this study is to review the state-of-the-art technologies towards privacy and security in the E-Commerce platforms. The identified cryptographic technologies were also discussed with the rational standpoint to understand the viability to apply in the E-Commerce operations. The study concludes with an enlightening path from which the E-Commerce merchants can be vigilant on data privacy and security in future.

IoT Big Data Security, Privacy and Challenges Related to Smart Grid

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization

In this fast growing data universe, data generation and data storage are moving into the next-generation process by generating petabytes and gigabytes in an hour. This leads to data accumulation where privacy and preservation are certainly misplaced. This data contains some sensitive and high privacy data which is to be hidden or removed using hashing or anonymization algorithms. In this article, the authors propose a hybrid k anonymity algorithm to handle large scale aircraft datasets with combined concepts of Big Data analytics and privacy preservation of storing the dataset with the help of MapReduce. This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.

Reconsidering big data security and privacy in cloud and mobile cloud systems

Large scale distributed systems in particular cloud and mobile cloud deployments provide great services improving people's quality of life and organizational efficiency. In order to match the performance needs, cloud computing engages with the perils of peer-to-peer (P2P) computing and brings up the P2P cloud systems as an extension for federated cloud. Having a decentralized architecture built on independent nodes and resources without any specific central control and monitoring, these cloud deployments are able to handle resource provisioning at a very low cost. Hence, we see a vast amount of mobile applications and services that are ready to scale to billions of mobile devices painlessly. Among these, data driven applications are the most successful ones in terms of popularity or monetization. However, data rich applications expose other problems to consider including storage, big data processing and also the crucial task of protecting private or sensitive information.In this work, first, we go through the existing layered cloud architectures and present a solution addressing the big data storage. Secondly, we explore the use of P2P Cloud System (P2PCS) for big data processing and analytics. Thirdly, we propose an efficient hybrid mobile cloud computing model based on cloudlets concept and we apply this model to health care systems as a case study. Then, the model is simulated using Mobile Cloud Computing Simulator (MCCSIM). According to the experimental power and delay results, the hybrid cloud model performs up to 75% better when compared to the traditional cloud models. Lastly, we enhance our proposals by presenting and analyzing security and privacy countermeasures against possible attacks.

The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey.

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

IoT Big Data Security and Privacy Versus Innovation

In this paper, we address the conflict in the collection, use, and management of Big Data at the intersection of security and privacy requirements and the demand of innovative uses of the data. This problem is exaggerated in the context of the Internet of Things (IoT). We propose a three-part decomposition of the design space, in order to clarify requirements and constraints. To reach this final analysis, we begin by clarifying the challenges in the design space: 1) there is a little agreement on what is meant by IoT, and in particular the security and privacy implications of different definitions; 2) we then consider the requirement and constraints on the Big Data that result from various IoT system designs; and 3) in parallel, we examine the intricacies of the demand for innovation from both the legal and economic perspectives. In this context, we then can decompose the set of drivers and objectives for security/privacy of data as well as innovation into: 1) the regulatory and social policy context; 2) economic and business context; and 3) technology and design context. By identifying these distinct objectives for the design of IoT Big Data management, we propose that more effective design and control is possible at the intersection of these forces, through an iterative process of review and redesign.

Enhanced Secured Map Reduce layer for Big Data privacy and security

The publication and dissemination of raw data are crucial elements in commercial, academic, and medical applications. With an increasing number of open platforms, such as social networks and mobile devices from which data may be collected, the volume of such data has also increased over time move toward becoming as Big Data. The traditional model of Big Data does not specify any level for capturing the sensitivity of data both structured and unstructured. It additionally needs to incorporate the notion of privacy and security where the risk of exposing personal information is probabilistically minimized. This paper introduced security and privacy layer between HDFS and MR Layer (Map Reduce) known as new proposed Secured Map Reduce (SMR) Layer and this model is known as SMR model. The core benefit of this work is to promote data sharing for knowledge mining. This model creates a privacy and security guarantee, resolve scalability issues of privacy and maintain the privacy-utility tradeoff for data miners. In this SMR model, running time and information loss have a remarkable improvement over the existing approaches and CPU and memory usage are also optimized.

Economic perspective analysis of protecting big data security and privacy

Abstract This paper investigates the economic perspective analysis of protecting security and privacy of big data. Traditionally, the pressing cyberthreats appear from emailed attachments. Recently, cyberattacks increasingly stealing or compromising data and are the potentials for physical damage to critical infrastructure. The risks of the data breach or compromised data collection are often favored by potential financial benefits (e.g., blackmail, fraud, false information, intellectual property thefts, business competition). That is, an important factor for current and future economical investments is due to the motivation of cybercrime activities. In this paper, we first analyze a question about our effort on security and privacy in terms of economic perspectives. That is, do we need to protect big data in a secure, private, and most effective manner, while the growing amount of security threats, attacks, and data breaches together with the increasing market for security products arises? Secondly, we perform the investigation from several perspectives: the economic perspective of big data security and privacy, investment decisions, fighting cybercrimes through big data, and cyberinsurance for big data. Our objective is to provide economic justification of technical decisions taken to protect the big data and the amount of costs that organizations often spend for it.